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2019-3827

Project Title: 
Prediction of outcome and adverse events in antipsychotic treatment
Specific Aims of the Project: 

Primary objective: A. Identify patterns of predictors for outcome during antipsychotic treatment.
Secondary objective: B. Identify patterns of predictors for adverse events during antipsychotic treatment.

Endpoints: The following two groups of endpoints (1A-4A and 5B-10B) are clustered according to the two groups of objectives (A and B).
Primary endpoint:
1A. Pattern of predictive variables for symptom reduction during antipsychotic treatment (all antipsychotics pooled).
Secondary endpoints:
2A. Pattern of predictive variables for symptom reduction during antipsychotic treatment (assessed for each antipsychotic individually).
3A. Pattern of predictive variables for increase in global functioning during antipsychotic treatment (all antipsychotics pooled).
4A. Pattern of predictive variables for increase in global functioning during antipsychotic treatment (assessed for each antipsychotic individually).
5B/6B/7B. Pattern of predictive variables for occurrence/severity/duration of adverse events during antipsychotic treatment (all antipsychotics pooled).
8B/9B/10B. Pattern of predictive variables for occurrence/severity/duration of adverse events (assessed for each antipsychotic individually).

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation
Associated Trial(s): 

Application Status

Ongoing
Scientific Abstract: 

Background: Outcome and adverse events are the two primary factors when planning a safe and successful antipsychotic therapy. However, evidence is scarce regarding the prediction of outcome and adverse events in an individual patient.
Objective: Our aim is to predict the outcome and adverse events in antipsychotic treatment.
Study Design: We plan to predict the outcome and adverse events by implementing a machine learning approach in an individual participant data meta-analysis of randomized controlled trials (RCTs).
Participants: Schizophrenia, bipolar disorder, and schizoaffective disorder.
Main outcome measure: Our main outcome measure will be reduction of major symptoms (i.e. psychosis or mania).
Statistical analysis: Non-linear regression of the above-mentioned outcome measures will be carried out using state-of-the-art artificial neural networks. The model’s accuracy will be compared with alternative approaches including linear regression models and support vector regression.

Brief Project Background and Statement of Project Significance: 

Providing the appropriate antipsychotic substance in psychiatric disorders is a complex process that involves prediction of at least two key factors: outcome and adverse events1. The desired outcome should ideally outweigh potential adverse events. However, in clinical routine, prediction of these two factors in individual remains elusive2.
Predictors of outcome have been investigated in different clinical, social, and genetic domains2. Focussing on the clinical history, Kinon et al. investigated early response to an antipsychotic medication as a predictive factor for a later response3. Using data from five randomized controlled trials (n = 1077 schizophrenia patients), they showed that early non-response was a robust predictor of continued later lack of response3. Similarly, social factors likely influence the functional outcome in antipsychotic treatment. For instance, Köhler-Forsberg et al. reported that living with a partner was the strongest predictor of social functioning (assessed by Global Assessment of Functioning – GAF) after clozapine initiation in schizophrenia patients4. Pharmacogenetic investigations showed that a combination of six polymorphisms in neurotransmitter-receptor-related genes resulted in a significant 76.7% prediction of clozapine response and a sensitivity of 95% for satisfactory response5. Taken together, these findings indicate that multifactorial variables from different domains contribute to the outcome of antipsychotic treatment.
In addition to outcome, prediction of adverse events (AEs) is the other key factor when planning a safe and successful therapy. Even though AEs are frequent in antipsychotics6, little is known about their prediction according to individual patient characteristics7. Polypharmacy is a known risk factor for the occurrence of adverse events8. Furthermore, the risk for increased weight gain during treatment with olanzapine was reported to be threefold in subjects with at least one allele at each locus of leptin and leptin receptor8. Polymorphisms (A1 allele) of the dopamine D2 receptor gene Taq1 in females were associated with increased prolactine levels during treatment with bromperidol9.
The scarce literature highlights the need for research to optimize prediction of outcome and adverse events in antipsychotic treatment. Importantly, both measures depend on multiple variables (e.g. demographic, genetic, and clinical) most likely in a complex non-linear interaction, which cannot be captured in conventional linear regression models, where the dependent variable is predicted as a weighted sum of individual predictors. Neural networks represent the most advanced technique to tackle such non-linear regression problems. Employing these techniques, we aim at identifying complex patterns of predictors of treatment outcome and adverse events. This study could have a major impact on health of patients and help to identify crucial predictors of outcome and adverse events and may help to promote the development of personalized and precise therapeutic strategies.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

Participant-level data provided from randomized controlled trials (RCTs) on antipsychotic treatment in patients with schizophrenia, bipolar disorder, and schizoaffective disorder will be included. All routes of antipsychotic administration (e.g. oral and injection) will be included. The primary endpoint is six weeks treatment duration but durations from 4 to 12 weeks will also be included10.

Narrative Summary: 

The two main factors guiding the choice of a specific antipsychotic treatment are its outcome and risk of adverse events. A personalized therapeutic strategy would be based on prediction of these factors, where the treatment effect should ideally outweigh potential adverse events. However, in clinical routine, prediction of these two factors remains elusive. We intend to predict outcome and adverse events in patients with schizophrenia, schizoaffective disorder, and bipolar disorder treated with antipsychotics in randomized controlled trials using a machine learning approach trained on individual demographic, clinical, and laboratory parameters.

Project Timeline: 

• Milestone 1 at 0 months: Data preparation and implementation of Deep Learning neural network methods starts.
• Milestone 2 at 12 months: Analysis of objective A starts.
• Milestone 3 at 24 months: Analysis of objective B starts.
• Milestone 4 at 36 months: Analyses of objectives are completed and papers drafted.
The YODA project will be informed about the completion of each milestone and reports will be made available.

Dissemination Plan: 

To benefit both health professionals and patients we will present the study at internationally accredited conferences (e.g. symposia at the WPA) and make the study available in major medical journals (e.g. JAMA Psychiatry, American Journal of Psychiatry, Lancet Psychiatry).

Bibliography: 

1. Leucht, S. et al. Comparative efficacy and tolerability of 15 antipsychotic drugs in schizophrenia: a multiple-treatments meta-analysis. Lancet 382, 951–962 (2013).
2. Stern, S., Linker, S., Vadodaria, K. C., Marchetto, M. C. & Gage, F. H. Prediction of response to drug therapy in psychiatric disorders. Open Biol. 8, 180031–13 (2018).
3. Kinon, B. et al. Predicting response to atypical antipsychotics based on early response in the treatment of schizophrenia☆. Schizophrenia Research 102, 230–240 (2008).
4. Köhler-Forsberg, O., Horsdal, H. T., Legge, S. E., MacCabe, J. H. & Gasse, C. Predictors of Nonhospitalization and Functional Response in Clozapine Treatment. Journal of Clinical Psychopharmacology 37, 148–154 (2017).
5. Arranz, M. J. et al. Pharmacogenetic prediction of clozapine response. Lancet 355, 1615–1616 (2000).
6. Citrome, L. A Review of the Pharmacology, Efficacy and Tolerability of Recently Approved and Upcoming Oral Antipsychotics: An Evidence-Based Medicine Approach. CNS Drugs 27, 879–911 (2013).
7. Plesnicar, B. K. Personalized antipsychotic treatment: the adverse effects perspectives. Psychiatr Danub 22, 329–334 (2010).
8. Kadra, G. et al. Predicting parkinsonism side-effects of antipsychotic polypharmacy prescribed in secondary mental healthcare. Journal of Psychopharmacology 32, 1191–1196 (2018).
9. Mihara, K. et al. Relationship between Taq1 A dopamine D2 receptor (DRD2) polymorphism and prolactin response to bromperidol. Am. J. Med. Genet. 105, 271–274 (2001).
10. Samara, M. et al. Initial symptom severity of bipolar I disorder and the efficacy of olanzapine: a meta-analysis of individual participant data from five placebo-controlled studies. The Lancet Psychiatry 4, 859–867 (2017).
11. Kay, S., Fiszbein, A. & Opler, L. A. The Positive and Negative Syndrome Scale (PANSS) for schizophrenia. Schizophrenia Bulletin 2, 261–276 (1987).
12. Young, R. C., Biggs, J. T., Ziegler, V. E. & Meyer, D. A. A rating scale for mania: reliability, validity and sensitivity. The British Journal of Psychiatry 133, 429–435 (1978).
13. Guy, W. ECDEU assessment manual for psychopharmacology. (Rockville, Md., USA : U.S. Dept. of Health, Education, and Welfare, Public Health Service, Alcohol, Drug Abuse, and Mental Health Administration, National Institute of Mental Health, Psychopharmacology Research Branch, Division of Extramural Research Programs, 1976).
14. Gurevich, P. & Stuke, H. Gradient conjugate priors and multi-layer neural networks. arXiv:1707.07287v3 [stat.ML] 1–44 (2018).
15. Gurevich, P. & Stuke, H. Pairing an arbitrary regressor with an artificial neural network estimating aleatoric uncertainty. arXiv:1707.07287v3 [stat.ML] 1–29 (2018).
16. Chekroud, A. M. et al. Cross-trial prediction of treatment outcome in depression: a machine learning approach. The Lancet Psychiatry 3, 243–250 (2016).
17. Little, R. J. et al. The Prevention and Treatment of Missing Data in Clinical Trials. N Engl J Med 367, 1355–1360 (2012).

What is the purpose of the analysis being proposed? Please select all that apply.: 
Participant-level data meta-analysis:
Participant-level data meta-analysis uses only data from YODA Project
Research on clinical prediction or risk prediction
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

The main outcome is reduction in the total score of major symptoms (psychosis or mania) from baseline to endpoint of six (four to twelve) weeks post-baseline. All assessment time points in this timeframe will be included. We aim to implement the same score for each disorder:
• Psychosis: Positive and Negative Syndrome Scale (PANSS)11
• Mania: Young Mania Rating Scale (YMRS)12
Oral and long-acting injectable antipsychotics will be calculated separately.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

Potential predictors include variables derived from demographic data, clinical examinations, and laboratory investigations. Specifically, we aim at determining predictive combinations (patterns) of these predictors using artificial neural networks. We will investigate if these predictive combinations are similar for all investigated psychiatric disorders / medications or unique to specific disorders / medications.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

We will include additional variables / characteristics potentially associated with outcome and AEs. Global functioning (endpoint 3A and 4A) will be measured on the Clinical Global Impression (CGI) – scale13. Occurrence of AEs (Yes / No) will be measured according to trial documentation. Duration of AEs is defined as cumulative number of days the AE occurred. Severity of AEs is measured as mild / moderate / severe.

Statistical Analysis Plan: 

We will merge individual patient data from the RCTs provided by The YODA Project. Separate analyses will be made for each diagnosis (i.e. schizophrenia, bipolar disorder, and schizoaffective disorder). We will use Deep Learning neural network methods with emphasis on uncertainty quantification and robustness against outliers. Uncertainty quantification does not just allow to predict the expected treatment outcome and risk for adverse events, but also confidence intervals of the prediction14,15. To choose a specific treatment option together with a patient in the sense of informed consent and shared decision making, it is crucial to have a measure of the prediction's certainty on hand. Hence, applying and refining techniques for certainty quantification of individualized treatment predictions might constitute a substantial advance on the road to individualized treatment recommendations based on multiple patient characteristics from different domains. Outlier robustness is important since real-life data is always noisy and contains contaminated learning data, which might impede the proper learning of predictive patterns.
We plan to compare the predictions of the neural networks with other established machine learning regression techniques such as support vector regression and random forests. We will construct multiple test-train folds for repeated cross-validation through partition of the original cohort into a subset for training purposes and a subset for testing16. Model performance is captured by the root mean squared error (e.g. deviance between predicted and real treatment outcome) and data likelihood and will be examined in an independent cohort subset.
Missing data will be treated as recommended by Little et al.17: First we will register if reasons for missing data were documented and develop a primary set of assumptions about the cause for missing data17. Then the primary set of assumptions will be followed by multiple imputation by chained equations and robustness tested with a sensitivity analysis17.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/167">NCT00488319 - R076477PSZ3002 - A 2-Year, Open-Label, Single-Arm Safety Study of Flexibly Dosed Paliperidone Extended Release (1.5-12 mg/day) in the Treatment of Adolescents (12 to 17 Years of Age) With Schizophrenia</a></li><li><a href="/node/173">NCT01009047 - R076477PSZ3003 - A Randomized, Multicenter, Double-Blind, Active-Controlled, Flexible-Dose, Parallel-Group Study of the Efficacy and Safety of Prolonged Release Paliperidone for the Treatment of Symptoms of Schizophrenia in Adolescent Subjects, 12 to 17 Years of Age </a></li><li><a href="/node/174">NCT00645099 - R076477SCH3020 - A Prospective Randomized Open-label 6-Month Head-To-Head Trial to Compare Metabolic Effects of Paliperidone ER and Olanzapine in Subjects With Schizophrenia</a></li><li><a href="/node/175">NCT00518323 - R076477PSZ3001 - A Randomized, Multicenter, Double-Blind, Weight-Based, Fixed-Dose, Parallel-Group, Placebo-Controlled Study of the Efficacy and Safety of Extended Release Paliperidone for the Treatment of Schizophrenia in Adolescent Subjects, 12 to 17 Years of Age</a></li><li><a href="/node/177">NCT01606228 - R076477SCH3033 - An Open-Label Prospective Trial to Explore the Tolerability, Safety and Efficacy of Flexibly-Dosed Paliperidone ER among Treatment-Naive and Newly Diagnosed Patients with Schizophrenia</a></li><li><a href="/node/178">NCT00334126 - R076477SCH3015 - A Randomized, Double-blind, Placebo-controlled, Parallel Group Study to Evaluate the Efficacy and Safety of Paliperidone ER Compared to Quetiapine in Subjects With an Acute Exacerbation of Schizophrenia</a></li><li><a href="/node/179">NCT00086320 - R076477-SCH-301 - A Randomized, Double-blind, Placebo-controlled, Parallel-group Study With an Open-label Extension Evaluating Paliperidone Extended Release Tablets in the Prevention of Recurrence in Subjects With Schizophrenia</a></li><li><a href="/node/180">NCT00650793 - R076477-SCH-703 - A Randomized, DB, PC and AC, Parallel Group, Dose-Response Study to Evaluate the Efficacy and Safety of 3 Fixed Dosages of Extended Release OROS Paliperidone (6, 9, 12 mg/Day) and Olanzapine (10 mg/Day), With Open-Label Extension, in the Treatment of Subjects With Schizophrenia - Open Label Phase</a></li><li><a href="/node/181">NCT00589914 - R092670PSY3006 - A Randomized, Double-Blind, Parallel-Group, Comparative Study of Flexible Doses of Paliperidone Palmitate and Flexible Doses of Risperidone Long-Acting Intramuscular Injection in Subjects With Schizophrenia</a></li><li><a href="/node/182">NCT00604279 - R092670PSY3008 - A Randomized, Open-Label, Parallel Group Comparative Study of Paliperidone Palmitate (50, 100, 150 mg eq) and Risperidone LAI (25, 37.5, or 50 mg) in Subjects with Schizophrenia</a></li><li><a href="/node/190">NCT00590577 - R092670PSY3007 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Dose Response Study to Evaluate the Efficacy and Safety of 3 Fixed Doses (25 mg eq., 100 mg eq., and 150 mg eq.) of Paliperidone Palmitate in Subjects With Schizophrenia</a></li><li><a href="/node/191">NCT00111189 - R092670PSY3001 - A Randomized Double-blind Placebo-controlled Parallel Group Study Evaluating Paliperidone Palmitate in the Prevention of Recurrence in Patients With Schizophrenia. Placebo Consists of 20% Intralipid (200 mg/mL) Injectable Emulsion</a></li><li><a href="/node/192">NCT00210717 - R092670PSY3002 - A Randomized, Double-Blind, Parallel Group, Comparative Study of Flexibly Dosed Paliperidone Palmitate (25, 50, 75, or 100 mg eq.) Administered Every 4 Weeks and Flexibly Dosed RISPERDAL CONSTA (25, 37.5, or 50 mg) Administered Every 2 Weeks in Subjects With Schizophrenia</a></li><li><a href="/node/193">NCT00119756 - R092670PSY3005 - A Randomized, Crossover Study to Evaluate the Overall Safety and Tolerability of Paliperidone Palmitate Injected in the Deltoid or Gluteus Muscle in Patients With Schizophrenia</a></li><li><a href="/node/194">NCT00210548 - R092670PSY3003 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Dose-Response Study to Evaluate the Efficacy and Safety of 3 Fixed Doses (50 mg eq., 100 mg eq., and 150 mg eq.) of Paliperidone Palmitate in Subjects With Schizophrenia</a></li><li><a href="/node/195">NCT00101634 - R092670PSY3004 - A Randomized, Double-blind, Placebo-controlled, Parallel-group, Dose-response Study to Evaluate the Efficacy and Safety of 3 Fixed Doses (25 mg eq, 50 mg eq, and 100 mg eq) of Paliperidone Palmitate in Patients With Schizophrenia</a></li><li><a href="/node/196">NCT00391222 - RISBMN3001 - A Randomized, Double Blind, Placebo and Active Controlled Parallel Group Study to Evaluate the Efficacy and Safety of Risperidone Long-acting Injectable (LAI) for the Prevention of Mood Episodes in the Treatment of Subjects With Bipolar I Disorder</a></li><li><a href="/node/197">NCT00034749 - RIS-USA-231 - The Efficacy and Safety of Risperidone in Adolescents With Schizophrenia: a Comparison of Two Dose Ranges of Risperidone</a></li><li><a href="/node/198">NCT00076115 - RIS-BIM-301 - Research on the Effectiveness of Risperidone in Bipolar Disorder in Adolescents and Children (REACH): A Double-Blind, Randomized, Placebo-Controlled Study of the Efficacy and Safety of Risperidone for the Treatment of Acute Mania in Bipolar I Disorder</a></li><li><a href="/node/199">NCT00132678 - RISBIM3003 - A Randomized, Double-blind, Placebo-controlled Study to Explore the Efficacy and Safety of Risperidone Long-acting Intramuscular Injectable in the Prevention of Mood Episodes in Bipolar 1 Disorder, With Open-label Extension</a></li><li><a href="/node/200">NCT00094926 - RIS-BIP-302 - A Prospective, Randomized, Double-blind, Placebo-controlled Study of the Effectiveness and Safety of RISPERDAL CONSTA Augmentation in Adult Patients With Frequently-relapsing Bipolar Disorder</a></li><li><a href="/node/296">NCT00397033 - R076477SCA3001 - A Randomized, Double-blind, Placebo-controlled, Parallel-group Study to Evaluate the Efficacy and Safety of Two Dosages of Paliperidone ER in the Treatment of Patients With Schizoaffective Disorder</a></li><li><a href="/node/297">NCT00412373 - R076477SCA3002 - A Randomized, Double-blind, Placebo-controlled, Parallel- Group Study to Evaluate the Efficacy and Safety of Flexible-dose Paliperidone ER in the Treatment of Patients With Schizoaffective Disorder</a></li><li><a href="/node/495">Multiple - OPTICS Trial Bundle</a></li><li><a href="/node/548">NCT00249132 - RIS-INT-3 - A Canadian multicenter placebo-controlled study of fixed doses of risperidone and haloperidol in the treatment of chronic schizophrenic patients</a></li><li><a href="/node/562">NCT00216476 - RISSCH3001 - CONSTATRE: Risperdal® Consta® Trial of Relapse Prevention and Effectiveness</a></li><li><a href="/node/563">NCT00216580 - RIS-PSY-301 - An Open-label Trial of Risperidone Long-acting Injectable in the Treatment of Subjects With Recent Onset Psychosis</a></li><li><a href="/node/576">NCT00253162 - RIS-INT-69 - The Efficacy And Safety Of Flexible Dose Ranges Of Risperidone Versus Placebo Or Haloperidol In The Treatment Of Manic Episodes Associated With Bipolar I Disorder</a></li><li><a href="/node/589">NCT00378092 - CR011992, RISSCH3024 - A Prospective Study of the Clinical Outcome Following Treatment Discontinuation After Remission in First-Episode Schizophrenia</a></li><li><a href="/node/622">NCT00299715 - R076477-BIM-3001 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Dose-Response, Multicenter Study to Evaluate the Efficacy and Safety of Three Fixed Doses of Extended-Release Paliperidone in the Treatment of Subjects With Acute Manic and Mixed Episodes Associated With Bipolar I Disorder</a></li><li><a href="/node/623">NCT00309699 - R076477-BIM-3002 - A Randomized, Double-Blind, Active- and Placebo-Controlled, Parallel-Group, Multicenter Study to Evaluate the Efficacy and Safety of Flexibly-Dosed, Extended-Release Paliperidone Compared With Flexibly-Dosed Quetiapine and Placebo in the Treatment of Acute Manic and Mixed Episodes Associated With Bipolar I Disorder</a></li><li><a href="/node/624">NCT00309686 - R076477-BIM-3003 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Multicenter Study to Evaluate the Efficacy and Safety of Flexibly-Dosed Extended-Release Paliperidone as Adjunctive Therapy to Mood Stabilizers in the Treatment of Acute Manic and Mixed Episodes Associated With Bipolar I Disorder</a></li><li><a href="/node/625">NCT00752427 - R076477-SCH-702 - 24 week extension of NCT00085748: A Randomized, 6-Week Double-Blind, Placebo-Controlled Study With an Optional 24-Week Open-Label Extension to Evaluate the Safety and Tolerability of Flexible Doses of Paliperidone Extended Release in the Treatment of Geriatric Patients With Schizophrenia</a></li><li><a href="/node/626">NCT00077714 - R076477-SCH-304 - A Randomized, Double-blind, Placebo- and Active-controlled, Parallel-group, Dose-response Study to Evaluate the Efficacy and Safety of 2 Fixed Dosages of Paliperidone Extended Release Tablets and Olanzapine, With Open-label Extension, in the Treatment of Patients With Schizophrenia</a></li><li><a href="/node/627">NCT00083668 - R076477-SCH-305 - A Randomized, Double-blind, Placebo- and Active-controlled, Parallel-group, Dose-response Study to Evaluate the Efficacy and Safety of 3 Fixed Dosages of Paliperidone Extended Release (ER) Tablets and Olanzapine, With Open-label Extension, in the Treatment of Patients With Schizophrenia</a></li><li><a href="/node/628">NCT00074477 - R092670-SCH-201 - A Randomized, Double-Blind, Placebo-Controlled Study to Evaluate the Efficacy and Safety of 50 and 100 Mg-eq of Paliperidone Palmitate in Patients With Schizophrenia</a></li><li><a href="/node/638">NCT00078039 - R076477-SCH-303 - Trial Evaluating Three Fixed Dosages of Paliperidone Extended-Release (ER) Tablets and Olanzapine in the Treatment of Patients With Schizophrenia</a></li><li><a href="/node/704">NCT00085748 - R076477-SCH-302 - A Randomized, 6-Week Double-Blind, Placebo-Controlled Study With an Optional 24-Week Open-Label Extension to Evaluate the Safety and Tolerability of Flexible Doses of Paliperidone Extended Release in the Treatment of Geriatric Patients With Schizophrenia</a></li><li><a href="/node/853">NCT00249236 - RIS-IND-2/CR006064 - The Efficacy And Safety Of Flexible Dosage Ranges Of Risperidone Versus Placebo In The Treatment Of Manic Or Mixed Episodes Associated With Bipolar I Disorder</a></li><li><a href="/node/855">NCT00250367 - RIS-INT-46/CR006058 - The Safety And Efficacy Of Risperdal (Risperidone) Versus Placebo As Add-On Therapy To Mood Stabilizers In The Treatment Of The Manic Phase Of Bipolar Disorder</a></li><li><a href="/node/857">NCT00088075 - RIS-SCH-302/CR003370 - A Randomized, Double-Blind, Placebo-Controlled Clinical Study of the Efficacy and Safety of Risperidone for the Treatment of Schizophrenia in Adolescents</a></li><li><a href="/node/858">RIS-USA-1 (RIS-USA-9001) - Risperidone versus haloperidol versus placebo in the treatment of schizophrenia</a></li><li><a href="/node/859">NCT00253149 - RIS-USA-102/CR006040 - The Safety And Efficacy Of Risperdal (Risperidone) Versus Placebo Versus Haloperidol As Add-On Therapy To Mood Stabilizers In The Treatment Of The Manic Phase Of Bipolar Disorder</a></li><li><a href="/node/860">NCT00253136 - RIS-USA-121/CR006055 - Risperidone Depot (Microspheres) vs. Placebo in the Treatment of Subjects With Schizophrenia</a></li><li><a href="/node/863">NCT00257075 - RIS-USA-239/CR006052 - The Efficacy And Safety Of Flexible Dosage Ranges Of Risperidone Versus Placebo In The Treatment Of Manic Episodes Associated With Bipolar I Disorder</a></li><li><a href="/node/864">RIS-USA-240 - The efficacy and safety of flexible dose ranges of risperidone vs. Placebo or divalproex sodium in the treatment of manic or mixed episodes associated with bipolar 1 disorder</a></li><li><a href="/node/866">RIS-USA-72 - The safety and efficacy of risperidone 8 mg qd and 4 mg qd compared to placebo in the treatment of schizophrenia</a></li><li><a href="/node/867">NCT01529515 - R092670PSY3012  - A Randomized, Multicenter, Double-Blind, Relapse Prevention Study of Paliperidone Palmitate 3 Month Formulation for the Treatment of Subjects With Schizophrenia</a></li><li><a href="/node/868">NCT01193153 - R092670SCA3004 - A Randomized, Double-Blind, Placebo-Controlled, Parellel-Group Study of Paliperidone Palmitate Evaluating Time to Relapse in Subjects With Schizoaffective Disorder </a></li><li><a href="/node/869">NCT01662310 - R076477-SCH-3041 - Paliperidone Extended Release Tablets for the Prevention of Relapse in Subjects With Schizophrenia: A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group Study</a></li><li><a href="/node/870">NCT00490971 - R076477BIM3004 - A Randomized, Double-Blind, Active- and Placebo-controlled, Parallel-group, Multicenter Study to Evaluate the Efficacy and Safety of Extended-Release Paliperidone as Maintenance Treatment After an Acute Manic or Mixed Episode Associated With Bipolar I Disorder</a></li><li><a href="/node/871">NCT00524043 - R076477SCH4012 - A Randomized, Double-Blind, Placebo- and Active-Controlled, Parallel-Group Study to Evaluate the Efficacy and Safety of a Fixed Dosage of 1.5 mg/Day of Paliperidone Extended Release (ER) in the Treatment of Subjects With Schizophrenia</a></li><li><a href="/node/872">NCT00105326 - R076477-SCH-1010/CR002281 - A Double-blind, Placebo-controlled, Randomized Study Evaluating the Effect of Paliperidone ER Compared With Placebo on Sleep Architecture in Subjects With Schizophrenia</a></li><li><a href="/node/1032">NCT00645307 - R076477-SCH-701 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group Study With an Open-Label Extension Evaluating Extended Release OROS® Paliperidone in the Prevention of Recurrence in Subjects With Schizophrenia - Open Label Phase</a></li><li><a href="/node/1116">NCT00246246 - RIS-BIP-301 - A Randomized, Open-label Trial of RISPERDAL® CONSTA™ Versus Oral Antipsychotic Care in Subjects With Bipolar Disorder</a></li><li><a href="/node/2901">NCT00249223 - RIS-INT-61 - Risperidone Depot (Microspheres) vs. Risperidone Tablets - a Non-inferiority, Efficacy Trial in Subjects With Schizophrenia</a></li><li><a href="/node/3767">NCT01157351 - R092670SCH3006 - A Fifteen-month, Prospective, Randomized, Active-controlled, Open-label, Flexible Dose Study of Paliperidone Palmitate Compared With Oral Antipsychotic Treatment in Delaying Time to Treatment Failure in Adults With Schizophrenia Who Have Been Incarcerated</a></li><li><a href="/node/3768">NCT01081769 - R092670SCH3005 - A 24-month, Prospective, Randomized, Active-Controlled, Open-Label, Rater-Blinded, Multicenter, International Study of the Prevention of Relapse Comparing Long-Acting Injectable Paliperidone Palmitate to Treatment as Usual With Oral Antipsychotic Monotherapy in Adults With Schizophrenia</a></li><li><a href="/node/3769">NCT01281527 - R092670SCH3010 - A 6-month, Open Label, Prospective, Multicenter, International, Exploratory Study of a Transition to Flexibly Dosed Paliperidone Palmitate in Patients With Schizophrenia Previously Unsuccessfully Treated With Oral or Long-acting Injectable Antipsychotics</a></li><li><a href="/node/3770">NCT01051531 - R092670SCH3009 - Safety, Tolerability, and Treatment Response of Paliperidone Palmitate in Subjects With Schizophrenia When Switching From Oral Antipsychotics</a></li><li><a href="/node/3771">NCT01527305 - R092670SCH4009 - An Open-Label, Prospective, Non-Comparative Study to Evaluate the Efficacy and Safety of Paliperidone Palmitate in Subjects With Acute Schizophrenia</a></li><li><a href="/node/3772">NCT01299389 - PALM-JPN-4 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Fixed-Dose, Multicenter Study of JNS010 (Paliperidone Palmitate) in Patients With Schizophrenia</a></li><li><a href="/node/3773">NCT01258920 - PALM-JPN-5 - A Long-Term, Open-Label Study of Flexibly Dosed Paliperidone Palmitate Long-Acting Intramuscular Injection in Japanese Patients With Schizophrenia</a></li><li><a href="/node/3804">NCT00216671 - RISSCH4045 - Early Versus Late Initiation of Treatment With Risperdal Consta in Subjects With Schizophrenia After an Acute Episode</a></li><li><a href="/node/3805">NCT00369239 - RISSCH4043 - Is Premorbid Functioning a Predictor of Outcome in Patients With Early Onset Psychosis Treated With Risperdal Consta?</a></li><li><a href="/node/3806">NCT00216632 - RISSCH4026 - Treatment Success in Patients Requiring Treatment Change From Olanzapine to Risperidone Long Acting Injectable (TRESOR)</a></li><li><a href="/node/3807">NCT00236379 - RIS-USA-275 - A Six-month, Double-blind, Randomized, International, Multicenter Trial to Evaluate the Glucoregulatory Effects of Risperidone and Olanzapine in Subjects With Schizophrenia or Schizoaffective Disorder</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2019

2018-3813

Project Title: 
External validation of a prognostic nomogram for first-line therapy in metastatic castration-resistant prostate cancer
Specific Aims of the Project: 

OVERALL AIMS:
To validate the prognostic value of the Armstrong nomogram15 for survival estimation in metastatic castration-resistant prostate cancer patients undergoing first-line treatment with abiraterone + prednisone or placebo + prednisone.

SPECIFIC ENDPOINTS:
Primary Endpoint:
- Association of prognostic risk groups (low, intermediate, high) with overall survival.

Secondary Endpoints:
- Association of prognostic risk groups (low, intermediate, high) with radiographic progression-free survival (rPFS).
- Association of prognostic risk groups (low, intermediate, high) with PSA progression-free survival (PSA-PFS).
- Evaluation of the prognostic ability of the nomogram score as a continuous variable.

Exploratory Endpoints:
- Impact of trial treatment (abiraterone vs placebo) on OS, rPFS and PSA-PFS in each of the prognostic risk groups.
- Evaluation of baseline quality of life (QoL) scores (FACT-P, BPI-SF), QoL response and time to QoL deterioration in each of the prognostic risk groups.
- Patterns of disease progression in each of the prognostic risk groups.

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Application Status

Ongoing
Scientific Abstract: 

Background: Currently, several life-prolonging therapies have been approved for metastatic castration-resistant prostate cancer (mCRPC) patients, however, evidence on the optimal sequence is lacking, and prognosis assessment remains an important issue. Recently, Armstrong et al have developed a novel prognostic nomogram for men with mCRPC treated with first-line enzalutamide. Nonetheless, the prognostic value of this model has not been analysed in mCRPC patients treated at first-line with abiraterone.

Objective: To assess the prognostic value of Armstrong nomogram for survival estimation in mCRPC patients treated in the COU-AA-302 trial.

Study Design: Retrospective cohort study

Participants: mCRPC patients treated on a prospective randomized clinical trial of abiraterone plus prednisone vs placebo plus prednisone in first-line of mCRPC (COU-AA-302)

Main Outcome Measures: Overall survival (OS), progression-free survival (PFS)

Statistical Analysis: Baseline risk scores according to the Armstrong nomogram will be calculated for each patient. Cox proportional-hazards (Cox-PH) models will be used to test the association of baseline risk scores / risk groups (low, intermediate, high) with OS and PFS. The prognostic ability of the models will be evaluated through Uno’s inverse-probability weighted c-index and time-dependent ROC AUC values. The impact of treatment arm in each of the risk groups (stratified Cox-PH models) and the association with baseline quality of life measures (linear and logistic regression models) will also be evaluated.

Brief Project Background and Statement of Project Significance: 

Currently, there are several options available for the treatment of metastatic Castration Resistant Prostate Cancer (mCRPC)(1-8), however, evidence on the optimal treatment sequence is lacking. Traditionally, the selection of strategies is based largely in clinical symptoms, comorbidities, expected side-effects and preferences by the patient.

It is important to assess the prognosis before starting a new therapy, in order to counsel patients about their long-term outcomes, and to guide treatment selection.
In this setting, prognostic models are useful tools that estimate the risk for disease-related mortality(9), and can play an important role for stratification and patient selection in clinical trials.
Prognostic models and nomograms for mCRPC patients have been developed in localized disease, and those for mCRPC were developed in patients receiving first- and second-line treatment(10-14). They contain different variables including both tumour and host factors.

Recently, Armstrong et al(15) have developed a novel nomogram that provides prognostic information using data collected from the PREVAIL trial, which compared enzalutamide with placebo in first-line of mCPRC patients. The model contains 11 known prognostic variables, including albumin, alkaline phosphatase (ALP), haemoglobin, lactate dehydrogenase (LDH), prostate specific antigen (PSA), number of bone metastases, presence if pain, pattern of spread, time since diagnosis, treatment and neutrophil-to-lymphocyte ratio (NLR). This model demonstrated a significant difference in overall survival (OS) for the low-risk group (HR: 0.20; 95% CI 0.14-0.29) and intermediate-risk group (HR: 0.40; 95% CI 0.30-0.53) compared with high-risk group.
However, these findings have not been analysed in other datasets of patients with mCRPC treated at first-line with abiraterone, and further external validation is needed.

We aim to:
i) Validate the prognostic nomogram carried out by Armstrong et al using an external dataset of patients treated on a prospective randomized clinical trial of abiraterone plus prednisone vs placebo plus prednisone in first-line of mCRPC (COU-AA-302)
ii) Evaluate the association between the different prognostic risk groups (low, intermediate or high) and overall survival in patients treated with abiraterone plus prednisone

If validated, this model could allow the assessment of risk in patients with mCRPC treated at first line with novel androgen receptor signalling inhibitors (ARSI), such as abiraterone or enzalutamide, through the use of factors that are routinely assessed in clinical practice, identifying subsets of patients with different survival outcomes.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

Data Source: COU-AA-302 datasets

Inclusion Criteria:
Patients treated with abiraterone + prednisone or placebo + prednisone in the COU-AA-302 trial.
Baseline clinical variables included in Armstrong nomogram available (albumin, ALP, Hemoglobin, LDH, NLR, Number of bone metastases, Presence of pain, pattern of spread, PSA, Time from diagnosis to randomisation, Treatment)

Narrative Summary: 

Although several life-prolonging therapies have been developed for metastatic castration-resistant prostate cancer (mCRPC), optimal sequence is unknown. In this setting, it is necessary to develop and validate prognostic models that reflect outcomes from currently treatments. In enzalutamide-treated patients at first-line, a novel nomogram that measures overall survival has been developed, identifying subsets of patients with different survival outcomes, through well-known prognostic variables. We aim to validate the prognostic value of this nomogram in mCRPC patients receiving abiraterone as first-line treatment. This could help in stratification and patient selection in clinical trials.

Project Timeline: 

- Project submission: December 2018
- Contract: January 2019
- Analysis: January-March 2019
- Abstract submission (ASCO 2019): February 2019
- Paper draft circulation: June-August 2019
- Paper submission: October-November 2019

Dissemination Plan: 

- Abstract presentation in ASCO 2019
- Submission of manuscript first-quartile oncology journals: Annals of Oncology, European Urology, Clinical Cancer Research.

Bibliography: 

1. Tannock IF, de Wit R, Berry WR, Horti J, Pluzanska A, Chi KN, et al. Docetaxel plus prednisone or mitoxantrone plus prednisone for advanced prostate cancer. N Engl J Med. 2004;351(15):1502-12.
2. de Bono JS, Oudard S, Ozguroglu M, Hansen S, Machiels JP, Kocak I, et al. Prednisone plus cabazitaxel or mitoxantrone for metastatic castration-resistant prostate cancer progressing after docetaxel treatment: a randomised open-label trial. Lancet. 2010;376(9747):1147-54.
3. de Bono JS, Logothetis CJ, Molina A, Fizazi K, North S, Chu L, et al. Abiraterone and increased survival in metastatic prostate cancer. N Engl J Med. 2011;364(21):1995-2005.
4. Ryan CJ, Smith MR, de Bono JS, Molina A, Logothetis CJ, de Souza P, et al. Abiraterone in metastatic prostate cancer without previous chemotherapy. N Engl J Med. 2013;368(2):138-48.
5. Scher HI, Fizazi K, Saad F, Taplin ME, Sternberg CN, Miller K, et al. Increased survival with enzalutamide in prostate cancer after chemotherapy. N Engl J Med. 2012;367(13):1187-97.
6. Beer TM, Armstrong AJ, Rathkopf DE, Loriot Y, Sternberg CN, Higano CS, et al. Enzalutamide in metastatic prostate cancer before chemotherapy. N Engl J Med. 2014;371(5):424-33.
7. Kantoff PW, Higano CS, Shore ND, Berger ER, Small EJ, Penson DF, et al. Sipuleucel-T immunotherapy for castration-resistant prostate cancer. N Engl J Med. 2010;363(5):411-22.
8. Parker C, Nilsson S, Heinrich D, Helle SI, O'Sullivan JM, Fossa SD, et al. Alpha emitter radium-223 and survival in metastatic prostate cancer. N Engl J Med. 2013;369(3):213-23.
9. Mallett S, Royston P, Waters R, Dutton S, Altman DG. Reporting performance of prognostic models in cancer: a review. BMC Med. 2010;8:21.
10. Smaletz O, Scher HI, Small EJ, Verbel DA, McMillan A, Regan K, et al. Nomogram for overall survival of patients with progressive metastatic prostate cancer after castration. J Clin Oncol. 2002;20(19):3972-82.
11. Halabi S, Lin CY, Small EJ, Armstrong AJ, Kaplan EB, Petrylak D, et al. Prognostic model predicting metastatic castration-resistant prostate cancer survival in men treated with second-line chemotherapy. J Natl Cancer Inst. 2013;105(22):1729-37.
12. Halabi S, Lin CY, Kelly WK, Fizazi KS, Moul JW, Kaplan EB, et al. Updated prognostic model for predicting overall survival in first-line chemotherapy for patients with metastatic castration-resistant prostate cancer. J Clin Oncol. 2014;32(7):671-7.
13. Chi KN, Kheoh T, Ryan CJ, Molina A, Bellmunt J, Vogelzang NJ, et al. A prognostic index model for predicting overall survival in patients with metastatic castration-resistant prostate cancer treated with abiraterone acetate after docetaxel. Ann Oncol. 2016;27(3):454-60.
14. Ryan CJ, Kheoh T, Li J, Molina A, De Porre P, Carles J, et al. Prognostic Index Model for Progression-Free Survival in Chemotherapy-Naive Metastatic Castration-Resistant Prostate Cancer Treated With Abiraterone Acetate Plus Prednisone. Clin Genitourin Cancer. 2017.
15. Armstrong AJ, Lin P, Higano CS, Sternberg CN, Sonpavde G, Tombal B, et al. Development and validation of a prognostic model for overall survival in chemotherapy-naive men with metastatic castration-resistant prostate cancer. Ann Oncol. 2018;29(11):2200-7.
16. Scher HI, Morris MJ, Basch E, Heller G. End points and outcomes in castration-resistant prostate cancer: from clinical trials to clinical practice. J Clin Oncol. 2011;29(27):3695-704.

What is the purpose of the analysis being proposed? Please select all that apply.: 
Research that confirms or validates previously conducted research on treatment effectiveness
Research on clinical prediction or risk prediction
Supplementary Material: 
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

Main Outcome Measure

- Overall survival will be defined as time from randomization to death.

Secondary Outcome Measures

- Radiographic progression-free survival: time from randomization to radiographic progression* or death.
- PSA progression-free survival: time from randomization to PSA progression* or death.
- Clinical progression-free survival: time from randomization to clinical progression* or death.
- QoL endpoints: High vs low quality of life scores will be defined as values above and below the median QoL score (FACT-P, BPI-SF) in each of the datasets. A QoL response will be defined as an ≥ 10 point increase in FACT-P scores relative to baseline. A pain response will be defined as an increase in BPI-SF scores.

*Radiographic, clinical and PSA progression will be defined as per Prostate Cancer Working Group 2 criteria (16)

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

The main predictor in our study will be the prognostic score defined by Armstrong et al, which is calculated from a number of clinical variables (see Table 1)

The score will be evaluated as a continuous variable.

Risk factors included in the prognostic nomogram will also be categorized into three risk groups. Variables defined as continuous in the nomogram (albumin, haemoglobin, PSA, TDR) will be categorized as follows:
- PSA: > 50 ng/mL
- Albumin: < 4 g/dL
- Hemoglobin: < 12.5 g/dL
- TDR: < 60 months

Three risk groups will be categorized as defined by Armstrong et al:
- High risk: 7-10 risk variables
- Intermediate risk: 4-6 risk variables
- Low risk: 0-3 risk variables

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

In addition to variables included in the risk score (see “main predictor/independent variable”), the following variables will be collected:

Baseline variables:
- Treatment arm: categorical
- Ethnicity: categorical
- Age, height, weight: continuous
- Type of disease progression at baseline: categorical
- De novo metastatic disease: yes/no
- Time from LHRH treatment to trial treatment initiation
- Presence of bone, node, liver, other visceral metastases: yes/no
- Gleason Score: ordinal
- Prior surgery or radiation therapy to primary: yes/no
- Use of steroids at baseline

Baseline and at post-baseline time-points:
- ECOG PS: ordinal (0-4)
- Post-baseline radiographic evaluation (BS/CT scan): categorical
- Treatment related adverse events (graded according to CTCAE)
- FACT-P, and BPI-SF scores at baseline and at each post-treatment and follow-up visit
- Post-progression lines of treatment

Statistical Analysis Plan: 

- A descriptive analysis of endpoints and baseline covariates will be performed. Results will be presented as the median and interquartile range (IQR) for continuous variables and as number and percentage frequency for categorical variables.
- The Kaplan-Meier method will be used to estimate median survival times (OS, rPFS, cPFS, time to QoL deterioration) and 95% confidence intervals, in months.
- Cox proportional-hazards (Cox-PH) models will be used to test the association of baseline risk score (continuous variable) or baseline risk groups (low, intermediate, high) with overall survival and progression-free survival (radiographic, PSA and clinical progression-free survival). Tests of proportionality based on Schoenefeld residuals will be applied to test the proportional hazards assumption.
- The prognostic ability of the prognostic scores will be evaluated by calculating Uno’s inverse- probability weighted c-index and time-dependent incident dynamic ROC AUC curve values of each of the Cox-PH models.
- The impact of treatment arm (abiraterone vs placebo) will be evaluated through a stratified Cox-PH analysis (stratified by risk group: low, intermediate, high) incorporating treatment arm as a covariate. The significance of the interaction factor between treatment arm and risk score in Cox-PH models will also be determined.
- Linear regression models will be used to determine the association between baseline PRO scores (FACT-P, BPI-SF) when determined as a continuous variable with the prognostic score (nomogram) when evaluated as a continuous variable. Spearman’s correlation coefficients will be calculated.
- Logistic regression models will be used to determine the association between baseline PRO scores (FACT-P, BPI-SF) when defined as a categorical (“high” vs “low”) with Armstrong baseline risk categories (“high”, “intermediate”, “low”). Odds ratio estimates and 95% confidence intervals will be calculated.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/1115">NCT00887198 - COU-AA-302 - A Phase 3, Randomized, Double-blind, Placebo-Controlled Study of Abiraterone Acetate (CB7630) Plus Prednisone in Asymptomatic or Mildly Symptomatic Patients With Metastatic Castration-Resistant Prostate Cancer</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2019

2018-3765

Project Title: 
Predictors of therapeutic and adverse effect outcomes of golimumab
Specific Aims of the Project: 

Specific hypothesis
The hypothesis of this project is that clinical prediction models of therapeutic and adverse effects outcomes of golimumab and relevant comparator medicines can be developed from available clinical trial data to enable informed-decisions to be made regarding golimumab therapy in patients with autoimmune diseases.
Specific aims
1. Identify baseline and on-treatment predictors and develop clinical prediction models of the key adverse effects of golimumab and relevant comparator medicines when used in the treatment of autoimmune diseases (RA, PA, AS, and UC).
2. Identify baseline and on-treatment predictors and develop clinical prediction models of the key therapeutic outcomes (response / progression, quality of life and survival) of golimumab and relevant comparator medicines when used in the treatment of autoimmune diseases.
3. Evaluate the heterogeneity of treatment (golimumab versus relevant comparator medicines (e.g methotrexate in RA patients) adverse effects and therapeutic outcomes according to model predicted risk.
4. Identify baseline and on-treatment predictors and develop clinical prediction models of patient exposure to golimumab and relevant comparator medicines.

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation
Associated Trial(s): 

Application Status

Ongoing
Scientific Abstract: 

Background: Golimumab is an important treatment option for various autoimmune diseases. However, response and toxicity to golimumab can be unpredictable with ~ 40% of patients not responding or experiencing toxicity.
Objectives: To develop predictive models of therapeutic and adverse effect outcomes in patients using golimumab to treat various autoimmune diseases. Being able to identify the profile of expected therapeutic and adverse effect outcomes may enable patients and clinicians to make better decisions regarding whether to commence, continue, discontinue or change dosing of golimumab.
Study design: Pooled analysis of individual participant data from studies investigating golimumab, and relevant comparator arms for the treatment of patients with rheumatoid arthritis (RA), psoriatic arthritis (PA), ankylosing spondylitis (AS) and ulcerative colitis (UC)
Participants: Patients with RA, PA, AS, or UC treated with golimumab or relevant comparator arms
Main outcome measure(s): Measures of therapeutic (e.g. remission, response, quality of life and survival) and adverse effect outcomes in accordance with the respective diseases and / or medications
Statistics: Cox-proportional hazard/time-to-event models will be used to assess the association between potential predictors and the time to an adverse effect or response/progression/survival. The association of potential predictors with binary outcomes will be modelled using logistic regression. Longitudinal analysis will be used to assess the patterns of longitudinal changes of key continuous variables

Brief Project Background and Statement of Project Significance: 

Golimumab is a Tumour Necrosis Factor inhibitor that belongs to a class of medicines called biological disease modifying antirheumatic drugs (bDMARDs). Biological DMARDs, including golimumab, are now very commonly used by patients with RA, PA, AS, and UC and are expensive. Unfortunately, the effect of golimumab takes weeks to months to become apparent and in patients who do not respond (~40%), value time has been lost which leads to increased morbidity and mortality [1-4]. Further, golimumab is associated with potentially life-threatening toxicities. Therefore, more research is required to confirm and explore novel predictive markers of therapeutic and adverse effects of golimumab to ultimately help clinicians make informed decisions regarding the use of golimumab to treat autoimmune diseases.
In this project, clinical prediction models of therapeutic and adverse effects outcomes to golimumab and relevant comparator medicines (e.g. methotrexate in RA patients) will be developed from available data from RA, PS, AS, and UC. The analysis will identify and validate predictors of the most important adverse effects, and clinical/biological/patient predictors of therapeutic outcomes such as response/progression, quality of life and survival.
Ultimately developing clinical prediction models for golimumab in patients with autoimmune diseases could be used to make informed decisions as to whether to commence, continue, discontinue or change dosing of golimumab which can eventually lead to improved health outcomes and significant cost savings.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

To precisely and validly determine the relationship between potential predictors and outcomes of interest it is important to have the maximum sample size possible across a range of different study populations (an increased number of studies increases the population diversity and is thus more comparable to standard clinical practice). Therefore, all studies collecting baseline and follow-up clinical characteristic data, as well as adverse event or therapeutic outcome data for patients treated with golimumab and relevant comparator medicines for the treatment of RA, PS, AS, and UC have been selected (model building will use the per-protocol populations). Particular care has been taken to select studies that assess the target populations for golimumab (i.e. RA, PS, AS, and UC), rather than just every study that has examined golimumab. Data from the comparator arms will be required to understand the heterogeneity in treatment effect according to identified risk factors (analyses of the heterogeneity of treatment effects will use the intent-to-treat populations), and whether the risk factors identified are specific to golimumab or are common across multiple therapies.

Narrative Summary: 

There have been several important developments in the treatment of autoimmune diseases over the last decade, including the introduction of golimumab. However, response and toxicity to golimumab can be unpredictable. For example, ~ 40% of the eligible patients who initiate golimumab therapy for rheumatoid arthritis do not respond, while 60% experience some form of toxicity. Thus, more research is required to confirm and explore novel predictive markers of therapeutic and adverse effects of golimumab in the treatment of autoimmune diseases (e.g. rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, and ulcerative colitis).

Project Timeline: 

The project is expected to take 2 years from the date of data access and the research group is prepared to renegotiate access at 12 months intervals. Estimated start date 1 May 2019 with all analysis completed by 30 April 2021. Manuscripts will be drafted and submitted at each stage of the proposed project. Results will be reported back to YODA prior to manuscript acceptance

Dissemination Plan: 

Results of all completed analyses will be published in peer-reviewed journals and where possible also presented at scientific meetings. Manuscript(s) will be submitted as soon as possible following completion of the requisite analyses. Suitable journals include Arthritis and Rheumatology, Arthritis Care and Research, Rheumatology, Annals of the Rheumatic Diseases, and British Journal of Clinical Pharmacology.

Bibliography: 

1. Flamant, M., S. Paul, and X. Roblin, Golimumab for the treatment of ulcerative colitis. Expert opinion on biological therapy, 2017. 17(7): p. 879-886.
2. Wijbrandts, C. and P. Tak. Prediction of response to targeted treatment in rheumatoid arthritis. in Mayo Clinic Proceedings. 2017. Elsevier.
3. Inman, R.D., et al., Efficacy and safety of golimumab in patients with ankylosing spondylitis: results of a randomized, double‐blind, placebo‐controlled, phase III trial. Arthritis & Rheumatism, 2008. 58(11): p. 3402-3412.
4. Kavanaugh, A., et al., Golimumab in psoriatic arthritis: one‐year clinical efficacy, radiographic, and safety results from a phase III, randomized, placebo‐controlled trial. Arthritis & Rheumatism, 2012. 64(8): p. 2504-2517.

What is the purpose of the analysis being proposed? Please select all that apply.: 
New research question to examine treatment safety
Summary-level data meta-analysis:
Summary-level data meta-analysis uses only data from YODA Project
Participant-level data meta-analysis:
Participant-level data meta-analysis uses only data from YODA Project
Research on comparison group
Research on clinical prediction or risk prediction
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

Outcomes including response/progression/clinical remission (e.g. American College of Rheumatology Response Criteria (e.g.20/50/70), EULAR response classification, DAS28 scores, Psoriasis Area and Severity Index (PASI), Assessment in Spondyloarthritis International Society (ASAS) classification criteria (e.g. ASAS 20), change from baseline in the Bath Ankylosing Spondylitis Functional Index (BASFI), Simplified Disease Activity Index, Clinical Disease Activity Index Modified Total Sharp Score, quality of life changes (e.g. Health Assessment Questionnaire-Disability Index, Rheumatoid Arthritis Quality of Life questionnaire), treatment satisfaction questionnaire, pain, fatigue, survival, adverse event outcomes (clinician/patient reported adverse effects defined by grade and sentinel events [e.g. hospitalization/discontinuation]), and drug exposure (concentration). Where a metric is derived, the data required to calculate the score will be required (e.g. 28 Tender Joint Count, 28 Swollen Joint Count, Patient Global assessment of disease activity, Physician global assessment of disease activity, pain score, ESR and CRP). The most recent in scope data cuts of these variables are required.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

Most data collected within trial contains some information on the immune system, disease severity, prognosis, toxicity risk or drug exposure. Thus, it’s important to access all the baseline/pre-treatment and follow-up clinical/biological/patient characteristic data collected on an individual for any given study. Covariates to be explored include, but not limited to
• Characteristic data – e.g. age, sex, race/ethnicity, BMI, weight, disease duration prior to therapy initiation, smoking Hx, alcohol Hx, family Hx of disorders, and measures of performance/QOL/PRO
• Lab data: e.g. levels of ALB, BILI, full blood count (e.g. HGB, RBC, MCV, WBC, lymphocyte, neutrophil, monocyte, platelets), INR, blood glucose, HBA1C, creatinine, CRP, ESR, calcium, total protein, cholesterol (LDL, HDL, total TG), RF titre, anti-CCP titre and blood urea nitrogen
• Disease classification/common biomarker data, e.g. prior therapy, prior surgery, time to response / progression for previous therapies, time since diagnosis, number and sites of tender and swollen joints, line of therapy, joint space, pathological features of T-cells, shared epitope status, and disease / drug specific genotype data.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

• Other common predictors, e.g. concomitant medications (including use of rescue medicines such as corticosteroids), respiratory comorbidity (e.g. asthma), comorbid diseases (e.g. peripheral vascular disease, cerebrovascular disease, diabetes, Hepatitis C infection), simplified comorbidity score, organ dysfunction (e.g. liver, lung or renal), and other clinical, biological, vital statistics, laboratory, imaging, pharmacokinetic and patient-reported outcomes measures that are commonly collected in clinical trials and related to therapeutic and adverse outcomes.
• Post-baseline values. Post-baseline values (including longitudinal relationships/patterns) can be useful early markers of therapeutic outcomes or adverse events. Variables include time-varying clinical (including adverse events such as immune related adverse events and comorbidities), radiological (e.g. joint space narrowing, erosions), biological/laboratory (e.g. haemoglobin, red cell count, WBC, Liver function tests, drug exposure/concentration), vital statistics (e.g. weight, heart rate, blood pressure), disease classification, patient-reported outcomes, and other common time-dependent predictor data.

Statistical Analysis Plan: 

Cox-proportional hazard / time-to-event models will be used to assess the association between potential predictors and the time to an adverse effect or response / progression / survival. Associations will be reported primarily as hazard ratios with 95% confidence intervals. The association of potential predictors with binary outcomes (e.g. yes / no) will be modelled using logistic regression and will be reported as odds ratios with 95% confidence intervals. Longitudinal analysis (e.g. linear and non-linear mixed effect modelling) will be used to assess the nature and patterns of longitudinal changes of key continuous variables (e.g. drug concentration, immune cell counts, number of swollen and tender joints).
The R Software and packages will be used for data preparation, modelling and graphical output. Non-linear mixed effect modelling of concentration and disease activity measures will be evaluated using the “R” Software (R Core Team) with Rtools (an Rtoolset), and the R packages ‘RxODE’, ‘nlmixr’, ‘pmetrics’ and ‘mrgsolve’. NONMEM will be used if available.
Potential predictors will be prioritised according to biological/clinical plausibility and prior evidence of association with the relevant outcome (adverse events, therapeutic response, drug exposure). Should multiple values of a covariate be recorded multiple times for a single visit (e.g. blood pressure) the mean of the multiple reads taken at each visit will be used. Crude associations will be reported based on univariate analysis (adjusting only for the clinical trial and where appropriate the medicines used), and adjusted associations based on a multivariable analysis. Continuous variables will be assessed for non-linear associations. Clinical prediction models will be developed using multivariable analysis. Penalisation method will be used to minimise the risk of model overfitting. Early markers of exposure, response and toxicity will be primarily evaluated using a landmark approach where possible. Landmark time will be dependent on the time points available in individual studies, and the time frame of changes in each specific predictor variable. As this analysis is primarily hypothesis generating and will require subsequent validation of any findings, no formal adjustment for multiple testing is intended. However, this limitation will be clearly stated in any publications of results. As it is expected that < 5% of data will be missing for most potential predictor variables a complete case analysis is planned. Should variables with substantial missing data be present, the pattern and likely cause of the missing data will be evaluated and if missing at random is reasonable to assume then imputation will be undertaken.
Analyses will include evaluating predictors of therapeutic and adverse outcomes for relevant comparator medicines. Analyses will also include evaluating the heterogeneity in toxicity incidence and therapeutic profiles according to modelled risk for golimumab as compared to relevant comparator arms (e.g. methotrexate). Such analyses will allow a better understanding of the benefits of golimumab, and whether the relationships identified are specific to golimumab, the comparator medicine or are common across patients.
Predictors that have a clinically meaningful (e.g. double the risk) effect on outcome and adverse effects will be of primary interest. Based upon a 30% incidence of toxicity, a sample size of approximately 180 is required to detect a predictor associated with a two-fold risk (α=0.05 with 80% power). Based upon an event rate of 40% during trial follow-up (e.g. for response), approximately 160 participants are required for 80% power to detect a predictor associated with a two-fold hazard of the event (α=0.05).

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/161">NCT00264537 - C0524T05 - A Multicenter, Randomized, Double-blind, Placebo-controlled Trial of Golimumab, a Fully Human Anti-TNFa Monoclonal Antibody, Administered Subcutaneously, in Methotrexate-naïve Subjects with Active Rheumatoid Arthritis</a></li><li><a href="/node/162">NCT00264550 - C0524T06 - A Multicenter, Randomized, Double-blind, Placebo-controlled Trial of Golimumab, a Fully Human Anti-TNFa Monoclonal Antibody, Administered Subcutaneously, in Subjects with Active Rheumatoid Arthritis Despite Methotrexate Therapy</a></li><li><a href="/node/163">NCT00265083 - C0524T09 - A Multicenter, Randomized, Double-blind, Placebo-controlled Trial of Golimumab, a Fully Human Anti-TNFa Monoclonal Antibody, Administered Subcutaneously, in Subjects with Active Ankylosing Spondylitis</a></li><li><a href="/node/164">NCT00299546 - C0524T11 - A Multicenter, Randomized, Double-blind, Placebo-controlled Trial of Golimumab, a Fully Human Anti-TNFa Monoclonal Antibody, Administered Subcutaneously in Subjects with Active Rheumatoid Arthritis and Previously Treated with Biologic Anti TNFa Agent(s)</a></li><li><a href="/node/165">NCT00361335 - C0524T12 - A Multicenter, Randomized, Double-blind, Placebo-controlled Trial of Golimumab, a Fully Human Anti-TNFa Monoclonal Antibody, Administered Intravenously, in Subjects with Active Rheumatoid Arthritis Despite Methotrexate Therapy</a></li><li><a href="/node/166">NCT00487539 - C0524T17 - A Phase 2/3 Multicenter, Randomized, Placebo-controlled, Double blind Study to Evaluate the Safety and Efficacy of Golimumab Induction Therapy, Administered Subcutaneously, in Subjects with Moderately to Severely Active Ulcerative Colitis</a></li><li><a href="/node/168">NCT01248780 - C0524T28 - A Phase 3, Multicenter, Randomized, Double-blind, Placebo-controlled Study Evaluating the Efficacy and Safety of Golimumab in the Treatment of Chinese Subjects with Active Rheumatoid Arthritis Despite Methotrexate Therapy</a></li><li><a href="/node/169">NCT01248793 - C0524T29 - A Phase 3, Multicenter, Randomized, Double-blind, Placebo-controlled Study Evaluating the Efficacy and Safety of Golimumab in the Treatment of Chinese Subjects with Ankylosing Spondylitis</a></li><li><a href="/node/557">NCT00265096 - C0524T08 - A Multicenter, Randomized, Double-blind, Placebo controlled Trial of Golimumab, a Fully Human Anti-TNFa Monoclonal Antibody, Administered Subcutaneously in Subjects with Active Psoriatic Arthritis</a></li><li><a href="/node/990">NCT00973479 - CNTO148ART3001 - A Multicenter, Randomized, Double-blind, Placebo-controlled Trial of Golimumab, an Anti-TNFalpha Monoclonal Antibody, Administered Intravenously, in Patients With Active Rheumatoid Arthritis Despite Methotrexate Therapy</a></li><li><a href="/node/1286">NCT00488631 - C0524T18 - A Phase 3 Multicenter, Randomized, Placebo-controlled, Double-blind Study to Evaluate the Safety and Efficacy of Golimumab Maintenance Therapy, Administered Subcutaneously, in Subjects With Moderately to Severely Active Ulcerative Colitis</a></li><li><a href="/node/3511">NCT02186873 - CNTO148AKS3001 - A Study of Golimumab in Participants With Active Ankylosing Spondylitis</a></li><li><a href="/node/3516">NCT02181673 - CNTO148PSA3001 - A Study of Golimumab in Participants With Active Psoriatic Arthritis</a></li><li><a href="/node/3521">NCT01004432 - CNTO148ART3002 - Golimumab in Rheumatoid Arthritis Participants With an Inadequate Response to Etanercept (ENBREL) or Adalimumab (HUMIRA)</a></li><li><a href="/node/3738">NCT01453725 - P07642  - A Multicenter, Randomized, Double-blind, Placebo-controlled Study of the Effect of Golimumab Administered Subcutaneously in Subjects With Active Axial Spondyloarthritis (Also Known as MK-8259-006-02)</a></li><li><a href="/node/3739">NCT00975130 - P06129 - An Open-Label Study Assessing the Addition of Subcutaneous Golimumab (GLM) to Conventional Disease-Modifying Antirheumatic Drug (DMARD) Therapy in Biologic-Naïve Subjects With Rheumatoid Arthritis (Part 1), Followed by a Randomized Study Assessing the Value of Combined Intravenous and Subcutaneous GLM Administration Aimed at Inducing and Maintaining Remission</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2019

2018-3745

Project Title: 
Association of Quality of Life Measures with Outcome in Metastatic Castration-resistant Prostate Cancer
Specific Aims of the Project: 

Overall Aims:
To determine :
- Association between baseline HRQoL and outcome in metastatic castration-resistant prostate cancer treated with abiraterone.
- Value of changes in HRQoL in metastatic castration-resistant prostate cancer after treatment with abiraterone and their association with outcome.

Specific Endpoints:

Primary Endpoint:
- Association of baseline FACT-P and BPI-SF scores and overall survival.
- Association of a decline in FACT-P and BPI-SF scores and overall survival.

Secondary Endpoints:
- Association of baseline FACT-P and BPI-SF scores with:
o Radiographic, PSA, clinical progression-free survival (rPFS, PSA-PFS, cPFS).
o Other baseline prognostic clinical variables.
o Treatment-related adverse events, skeletal-related events.
- Association of %changes in FACT-P and BPI-SF scores with PSA or RECIST response.

Exploratory Endpoints:
- To evaluate the efficacy of abiraterone over placebo in patients with high vs low baseline FACT-P or BPI-SF scores.
- Correlation between time to FACT-P and BPI-SF deterioration and PSA-PFS, rPFS and OS.
- Patterns of progression (PSA, Rx, clinical) in patients with high vs low baseline FACT-P or BPI-SF scores.
- To evaluate alternative cut-off points for QoL and pain response/progression.

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Application Status

Ongoing
Scientific Abstract: 

Background: improvement and preservation of quality of life (HRQoL) is an important goal in advanced prostate cancer treatment. FACT-P and BPI-SF are the most frequent patient reported outcomes (PROs) used in clinical trials. In both COU-AA-301 and COU-AA-302 trials, abiraterone improved QoL and delayed the time to QoL deterioration. However, the prognostic and predictive value of QoL PROs has not been studied in patients treated with novel hormonal agents.
Objective: to evaluate the prognostic and predictive impact of HRQoL PROs in advanced prostate cancer patients treated with abiraterone or placebo.
Study Design: retrospective cohort study.
Participants: mCRPC patients treated in the COU-AA-301 and COU-AA-302 trials, with PRO (FACT-P and/or BPI-SF) data.
Main Outcome Measures: Overall survival (OS), progression-free survival (PFS).
Statistical Analysis: The association of baseline PRO scores (FACT-P, BPI-SF) with other baseline known prognostic factors will be evaluated through linear o logistic regression models. We will evaluate the association of baseline PROs, as well as changes in PRO scores after treatment initiation with OS/PFS will be evaluated with uni- and multivariable (MV) Cox Proportional Hazards (PH) models. The prognostic value of each of the FACT-P subscales will be determined by calculating the c-indices. The predictive value will be evaluated through an interaction test between treatment arm and baseline HRQoL (high vs low). Known prognostic clinical factors will be included as covariates in each of the Cox-PH models.

Brief Project Background and Statement of Project Significance: 

Metastatic castration-resistant prostate cancer is a deadly disease. Despite recent advances in the systemic treatment of the disease, prolongation of survival and palliation of symptoms are still the main goals of therapy. There has been an increased interest in the impact of novel agents on the quality of life (HRQoL) of prostate cancer patients, with specific reports of the impact on HRQoL for most of the recently reported phase III clinical trials.1–5

HRQoL is measured through patient reported outcomes (PROs). The most frequently used PROs in prostate cancer are the Functional Assessment of Cancer Therapy-Prostate (FACT-P) questionnaire and the Brief Pain Inventory Short Form (BPI-SF). Additionally, fatigue has also been specifically assessed in the COU-AA-301 trial through the Brief Fatigue Inventory (BFI).

Abiraterone acetate has shown a significant benefit in overall survival in two large randomized trials in mCRPC, both of which included HRQoL outcomes as secondary objectives. COU-AA-301 patients had overall worse baseline quality of life (median BPI-SF of 3, mean baseline FACT-P 108) compared to COU-AA-302 participants (mean FACTP of 122, 65-69% of patients with BPI-SF score 0-1).6 Despite these differences, a consistent improvement of quality of life of abiraterone over placebo has been reported in all analysed endpoints of both trials. In COU-AA-301, abiraterone conferred an improved rate of pain palliation (44 vs. 27%; P = 0.002),7 faster time to palliation (5.6 vs. 13.7 mos; p = 0.0018) and more durable pain palliation (4.2 vs. 2.1 mos; p = 0.0056) as well as improved fatigue8 and FACT-P score improvement compared to placebo.9 In the COU-AA-302, abiraterone significantly improved the median time to a decline in the FACT-P score,10 as well as a median time to total HRQoL deterioration score.6

Data on the clinical significance of HRQoL measures in patients treated with abiraterone is scarce. On the other hand, in patients treated with docetaxel in the TAX-327 trial, an association of baseline pain with a significantly worse overall survival has been reported. A decrease in pain with treatment, but not an increase in QoL, was independently associated with survival.11 Similarly, data reported with enzalutamide on the AFFIRM and PREVAIL trials showed a significant association between baseline FACT-P scores and an increased survival, as well as significantly improved survival in patients that experienced a 10-point increase in FACT-P scores after starting on treatment.12

We aim to:
(a) Validate the association between QoL and survival previously reported in docetaxel and enzalutamide-treated patients
(b) Evaluate whether the benefit of abiraterone over placebo is different in patients with baseline good vs worsened quality of life.
(c) To compare the prognostic value of each of the specific QoL scales.

We anticipate our results will contribute to the growing body of evidence that emphasizes the prognostic value of baseline QoL measures, which may lead potentially to stratification by baseline QoL in clinical trials in the future and to the design of specific clinical trials addressing validated QoL endpoints.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

Data Source: COU-AA-301 and COU-AA-302 datasets.
Inclusion Criteria:
Patients treated with abiraterone + prednisone or placebo + prednisone in the COU-AA-301 and COU-AA-302 trials.
Survival >= 12 weeks.
Baseline PRO (FACT-P, BPI-SF or BFI) data available.

Narrative Summary: 

There is growing interest in the impact that treatments have on the quality of life (QoL) of advanced prostate cancer patients. QoL, measured through the FACT-P or BPI-SF questionnaires, has been consistently improved in recently reported trials. In enzalutamide-treated patients, a prognostic value of baseline FACT-P scores has been shown, highlighting its clinical significance. We aim to evaluate the association of baseline QoL with survival in abiraterone-treated patients, and to evaluate if the efficacy of abiraterone over placebo is equivalent in patients with different baseline QoL scores. We envision this may help the design of trials with specific QoL endpoints in the future.

Project Timeline: 

- Project submission: November 2018
- Contract: December 2018
- Analysis: January - March 2019
- Abstract Submission (ASCO 2019): February 2019 - Paper Draft circulation: June-July 2019
- Paper Submission: August 2019

Dissemination Plan: 

- Abstract presentation in ASCO 2019
- Submission of manuscript first-quartile oncology journals: Annals of Oncology, European Urology, Clinical Cancer Research

Bibliography: 

1. Patrick-Miller LJ, Chen YH, Carducci MA, et al. Quality of life (QOL) analysis from E3805, chemohormonal androgen ablation randomized trial (CHAARTED) in prostate cancer (PrCa). J Clin Oncol. 2016;34(2):3-4.
2. Cella D, Ivanescu C, Holmstrom S, Bui CN, Spalding J, Fizazi K. Impact of enzalutamide on quality of life in men with metastatic castration-resistant prostate cancer after chemotherapy: additional analyses from the AFFIRM randomized clinical trial. Ann Oncol. 2014;(October 2014):179-185. doi:10.1093/annonc/mdu510.
3. de Bono JSS, Hardy-Bessard A-C, Kim CSS, et al. PROSELICA: Health-related quality of life (HRQL) and post-hoc analyses for the phase 3 study assessing cabazitaxel 20 (C20) vs 25 (C25) mg/m2 post-docetaxel (D) in patients (pts) with metastatic castration-resistant prostate cancer (mCRPC). Ann Oncol. 2016;27(suppl_6):722PD. doi:10.1093/annonc/mdw372.06.
4. Loriot Y, Miller K, Sternberg CN, et al. Effect of enzalutamide on health-related quality of life, pain, and skeletal-related events in asymptomatic and minimally symptomatic, chemotherapy-naive patients with metastatic castration-resistant prostate cancer (PREVAIL): Results from a randomised, p. Lancet Oncol. 2015;16(5):509-521. doi:10.1016/S1470-2045(15)70113-0.
5. Harland S, Staffurth J, Molina A, et al. Effect of abiraterone acetate treatment on the quality of life of patients with metastatic castration-resistant prostate cancer after failure of docetaxel chemotherapy. Eur J Cancer. 2013;49(17):3648-3657. doi:10.1016/j.ejca.2013.07.144.
6. Basch E, Autio K, Ryan CJ, et al. Abiraterone acetate plus prednisone versus prednisone alone in chemotherapy-naive men with metastatic castration-resistant prostate cancer: patient-reported outcome results of a randomised phase 3 trial. Lancet Oncol. 2013;14(12):1193-1199. doi:10.1016/S1470-2045(13)70424-8.
7. de Bono JS, Logothetis CJ, Molina A, et al. Abiraterone and increased survival in metastatic prostate cancer. N Engl J Med. 2011;364(21):1995-2005. doi:10.1056/NEJMoa1014618.
8. Sternberg CN, Molina A, North S, et al. Effect of abiraterone acetate on fatigue in patients with metastatic castration-resistant prostate cancer after docetaxel chemotherapy. Ann Oncol. 2013;24(4):1017-1025. doi:10.1093/annonc/mds585.
9. Logothetis CJ, Basch E, Molina A, et al. Effect of abiraterone acetate and prednisone compared with placebo and prednisone on pain control and skeletal-related events in patients with metastatic castration-resistant prostate cancer: exploratory analysis of data from the COU-AA-301 randomised tri. Lancet Oncol. 2012;13(12):1210-1217. doi:10.1016/S1470-2045(12)70473-4.
10. Ryan CJ, Smith MR, de Bono JS, et al. Abiraterone in metastatic prostate cancer without previous chemotherapy. N Engl J Med. 2013;368(2):138-148. doi:10.1056/NEJMoa1209096.
11. Berthold DR, Pond GR, Roessner M, et al. Treatment of hormone-refractory prostate cancer with docetaxel or mitoxantrone: relationships between prostate-specific antigen, pain, and quality of life response and survival in the TAX-327 study. Clin cancer Res. 2008;14(9):2763-2767. doi:10.1158/1078-0432.CCR-07-0944.
12. Beer TM, Miller K, Tombal B, et al. The association between health-related quality-of-life scores and clinical outcomes in metastatic castration-resistant prostate cancer patients: Exploratory analyses of AFFIRM and PREVAIL studies. Eur J Cancer. 2017;87:21-29. doi:10.1016/j.ejca.2017.09.035.
13. Eisenhauer E a, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228-247. doi:10.1016/j.ejca.2008.10.026.
14. Scher HI, Halabi S, Tannock I, et al. Design and end points of clinical trials for patients with progressive prostate cancer and castrate levels of testosterone: recommendations of the Prostate Cancer Clinical Trials Working Group. J Clin Oncol. 2008;26(7):1148-1159. doi:10.1200/JCO.2007.12.4487.
15. Jeong HH, Seong SJ, Hyun ML, Choi YH, Kim S, Han YC. The functional assessment of cancer therapy-prostate (FACT-P) scales in men with prostate cancer: Reliability and validity of the Korean version. J Korean Med Sci. 2006;21(2):295-299. doi:10.3346/jkms.2006.21.2.295.
16. Cleeland CS, Ryan KM. Pain assessment: global use of the Brief Pain Inventory. Ann Acad Med Singapore. 1994;23(2):129-138.

What is the purpose of the analysis being proposed? Please select all that apply.: 
News research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations
Research that confirms or validates previously conducted research on treatment effectiveness
Research on clinical prediction or risk prediction
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

Main Outcome Measure
- Overall survival will be defined as the time from treatment initiation to death.

Secondary Outcome Measures
- Radiographic PFS: time from treatment initiation to radiographic progression or death.
- PSA PFS: time from treatment initiation to PSA progression or death.
- Clinical PFS: time from treatment initiation to clinical progression or death.
- PSA response: 30% decline in PSA from baseline at 12 weeks from treatment initiation, and at any time-point.
- Radiographic response: response as defined by RECIST criteria,13 only for patients with measurable disease at baseline.

Radiographic PFS, PSA-PFS and clinical PFS will be defined as per definitions on the COU-AA-301 and COU-AA-302 trials (Prostate Cancer Working Group 2 criteria).14

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

The FACT-P questionnaire comprises a general function status scale and a prostate cancer-specific (PCS) subscale, ranging from 0 to 156 (higher scores indicating better QoL).15 The BPI-SF measures individual items on a scale of 0–10, with lower scores representing lower levels of pain intensity or less interference of pain with activities of daily living.16

FACT-P and BPI-SF scores will be assessed as continuous variables, and as categorical variables, with “high” (good QoL) defined as values above the median, and “low” (worse QoL) values represented as values below the median in each of the datasets. Alternative cut-off points will also be assessed (exploratory endpoints)

A post-treatment “improvement” in FACT-P (QoL “response”) scores will be defined as an increase in 10 points from baseline scores. An increase in BPI-SF will be defined as a “pain” response. Alternative cut-off points will be assessed (exploratory endpoints).

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

Baseline variables:
- Treatment arm: categorical
- Ethnicity: categorical
- Age, height, weight: continuous
- Type of disease progression at baseline: categorical
- Time from LHRH treatment to trial treatment initiation
- Presence of bone, node, liver, other visceral metastases: yes/no - Gleason Score: ordinal
- Prior surgery or radiation therapy to primary: yes/no

Baseline and at post-baseline time-points:
- Hemoglobin, albumin, alkaline phosphatase, LDH, PSA: continuous. - ECOG PS: ordinal (0-4)
- Post-baseline radiographic evaluation (BS/CT scan): categorical
- Treatment related adverse events (graded according to CTCAE)

Statistical Analysis Plan: 

- A descriptive analysis of endpoints and baseline covariates will be performed. Results will be presented as the median and interquartile range (IQR) for continuous variables and as number and percentage frequency for categorical variables.
- The Kaplan-Meier method will be used to estimate median survival times (OS, rPFS, cPFS) and 95% confidence intervals, in months.
- Linear regression models will be used to determine the association between baseline PRO scores (FACT-P, BPI-SF) when determined as a continuous variable with other baseline categorial prognostic factors.
- Logistic regression models will be used to determine the association between baseline PRO scores (FACT-P, BPI-SF) when defined as a categorical (“high” vs “low”) with other baseline categorial prognostic factors. Odds ratio estimates and 95% confidence intervals will be calculated.
- Cox proportional-hazards (Cox-PH) models will be used to test the association of baseline PROs (FACT-P, BPI-SF) as well as post-treatment changes in PROs with overall survival and progression-free survival (radiographic, PSA and clinical progression-free survival). Other covariates that show a significant (p<0.05) association with survival in the univariable Cox-PH model will be included in the multivariable Cox-PH model. If a skewed distribution is observed in any of the continuous variables, logarithmic transformation may be performed. Tests of proportionality based on Schoenefeld residuals will be applied to test the proportional hazards assumption.
- The prognostic value (association with OS) of each of the FACT-P subscales will be determined by calculating specific Cox-PH models for each of the subscales.
- The performance of each of the ) of each of the Cox-PH models will be compared by calculating Uno’s inverse- probability weighted c-index and time-dependent incident dynamic ROC AUC curve values (established around the median survival of the dataset.
- The potential predictive value of baseline QoL PRO scores will be evaluating the interaction between treatment arm (abiraterone + prednisone or placebo + prednisone) and HRQoL (“high” or “low” score) by calculating the significance of the interaction factor in a Cox-PH model.

The COU-AA-301 dataset will be used as a test set, and the COU-AA-302 dataset will be used as a validation dataset. All analyses will be performed in the intent-to-treat populations initially, and separately in each of the trial study arms.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/304">NCT00638690 - COU-AA-301 - A Phase 3, Randomized, Double-Blind, Placebo-Controlled Study of Abiraterone Acetate (CB7630) Plus Prednisone in Patients With Metastatic Castration-Resistant Prostate Cancer Who Have Failed Docetaxel-Based Chemotherapy</a></li><li><a href="/node/1115">NCT00887198 - COU-AA-302 - A Phase 3, Randomized, Double-blind, Placebo-Controlled Study of Abiraterone Acetate (CB7630) Plus Prednisone in Asymptomatic or Mildly Symptomatic Patients With Metastatic Castration-Resistant Prostate Cancer</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2019

2018-3737

Project Title: 
Gender-based Differences in Response to Therapy in Inflammatory Bowel Disease
Specific Aims of the Project: 

Aim 1. To define gender-based differences in endoscopic response to biological therapy among patients with active CD or UC, in a pooled analysis of randomized clinical trials (RCTs)
Hypothesis 1. Female patients with CD or UC are less likely to achieve endoscopic remission than male patients around biological milestones such as menarche, child-bearing years and menopause.

Sub aim 1. To define gender-based differences in biochemical response to biological therapy among patients with active CD or UC

Sub aim 2. To determine if the age at initial diagnosis of CD or UC impacts the effect of gender on endoscopic remission

Aim 2. To define gender-based differences in clinical response to biological therapy among patients with active CD or UC, in a pooled analysis of RCTs
Hypothesis 2. Female patients with CD or UC are less likely to achieve clinical remission compared with male patients around biological milestones such as menarche, child-bearing years and menopause.

Sub aim 2. To determine if gender impacts clinical improvement with TNFalpha inhibitors, defined as decrease in HBI by >=3 points or partial Mayo by >=2 points, but not meeting criteria for remission

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation
Associated Trial(s): 

Application Status

Ongoing
Scientific Abstract: 

Background
IBD often necessitates systemic biologic therapy to achieve disease remission and avoid adverse outcomes. Risk stratification is key to precise and personalized therapy, and ensuring optimal patient outcomes. While gender-based differences in IBD are increasingly recognized, the effect of gender on therapeutic response is not known.

Objective
To define gender-based differences in response to biologic therapy in IBD

Study Design
We will conduct a pooled analysis of data from randomized clinical trials in IBD where the primary outcome was response to biologic therapy.

Participants
Patients in phases 2-4 randomized clinical trials on the efficacy of infliximab, golimumab and ustekinumab will be included when data on response to therapy, stratified by sex, is available

Main Outcome Measure(s)
Primary outcome (Aim 1): Endoscopic remission, defined as CD endoscopic index of severity (CDEIS) <3 (CD) or modified Mayo endoscopic sub-score (MMES) <=1 (UC)

Secondary outcome (Aim 1): Biochemical remission defined as normalization of C-reactive protein (CRP) or fecal calprotectin

Primary outcome (Aim 2): Clinical remission, defined as Harvey-Bradshaw Index (HBI) of <5 in CD or partial Mayo score of <=1 in UC

Statistical Analysis
We will pool comparable data and determine the summary study estimate with gender as a variable using descriptive and multivariable techniques. We will pool survival data by using logistic regression and perform stratified and sensitivity analyses to determine the impact of other variables on outcomes.

Brief Project Background and Statement of Project Significance: 

Background and Statement of Project Significance

Inflammatory bowel diseases (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), are chronic, progressive, and disabling inflammatory disorders of the gastrointestinal tract. Their pathogenesis is incompletely understood, but involves the complex interaction between environmental determinants, immune dysregulation, and gut dysbiosis in a genetically susceptible host (1, 2) IBD can present at any age, but tends to affect adolescents and young adults most frequently. Mucosal healing with early aggressive biologic therapy, e.g. tumor necrosis factor alpha (TNF-alpha) inhibitors, improves long-term outcomes and prevents complications (3). Conversely, lack of adequate therapy and resultant high inflammatory burden carries the risk of progressive intestinal injury and complications including colorectal neoplasia. However, not all patients benefit from biologic agents and remain at risk of disease complications. Therefore, defining predictors of response to biologics is critical to the development of precise, personalized treatment strategies to improve patient important outcomes. (1, 2)

We hypothesize that gender is one clinical factor that might affect response to biologic therapy in IBD. There are emerging epidemiological data implicating sex in IBD pathogenesis, although the exact mechanisms are yet to be defined. A recent pooled analysis of 17 population-based studies from Western industrialized countries found that males were at higher risk for CD until age 10-14 years, with a higher risk in females at ages 25-29 and >35 years (4). Hormonal contraceptives and pregnancy have also been associated with worsening of IBD, lending weight to the role of sex hormones in gender-specific IBD phenotype (5). Experimental evidence is also supportive (6, 7). While sparse data suggest lower response to TNF-alpha inhibitors in women (8) and higher likelihood of drug neutralizing anti-TNF alpha antibodies (9), no single study has adequately investigated gender-specific differences in therapeutic responses in IBD. The primary aim of the present proposal is to systematically and comprehensively define gender-based differences in response to IBD biologic therapy.

The Yale University Open Data Access Project (YODA) is a powerful resource as it provides open access to primary trial data for clinical research. Using such data, we will conduct a pooled analysis to delineate whether gender predicts or modifies response to therapy as pre-defined by standardized, objective measures. We will conduct several sensitivity and stratified analyses as detailed below.

This would be the first comprehensive study to determine the impact of gender on therapeutic response to biologic therapy in patients with IBD. Our findings have immediate clinical implications and are an important step forward towards targeted treatment algorithms based in evidence that are expected to result in improved patient outcomes. Furthermore, our findings will contribute to better patient education and ability to better manage patient expectations with therapy.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

This will be a pooled analysis of primary trial data included in YODA on the efficacy of biologics for CD and UC to determine if gender impacts response to therapy.

Inclusion: All phases 2-4 RCTs on the efficacy of infliximab and golimumab (TNF-alpha), and ustekinumab (interleukin-12/23 inhibitor); drugs for which sub-trial data is available through YODA and for which the results are stratified by sex.

Narrative Summary: 

Inflammatory bowel diseases (IBD), i.e., Crohn’s disease (CD) and ulcerative colitis (UC) are immunologically-mediated diseases with progressive intestinal injury if untreated (1,2). Identifying predictors of response is critical to improve outcomes (1,2).
A pooled analysis of 17 population-based studies found higher CD risk in females 25-29 and >35 years old (4). Hormonal therapy and pregnancy are associated with severe IBD (5). Experimental data is supportive (6,7). Sparse data suggest lower response to anti-TNF(8) and higher risk of anti-drug antibodies (9) in women.
We will conduct a pooled analysis of available data to delineate the impact of gender on response to therapy in IBD.

Project Timeline: 

• Submission of proposal to YODA, approval and access: 4-6 weeks
• Data extraction: 8 weeks
• Analysis: 8 weeks
• Manuscript writing: 12 weeks

Dissemination Plan: 

We intend to submit our final results and their interpretation as a manuscript to a high-impact journal in IBD and/or Gastroenterology.

Bibliography: 

1. Torres J, Mehandru S, Colombel JF, Peyrin-Biroulet L. Crohn's disease. Lancet (London, England). 2017;389(10080):1741-55.
2. Ungaro R, Mehandru S, Allen PB, Peyrin-Biroulet L, Colombel JF. Ulcerative colitis. Lancet (London, England). 2017;389(10080):1756-70.
3. Khanna R, Bressler B, Levesque BG, Zou G, Stitt LW, Greenberg GR, et al. Early combined immunosuppression for the management of Crohn's disease (REACT): a cluster randomised controlled trial. Lancet (London, England). 2015;386(10006):1825-34.
4. Shah SC, Khalili H, Gower-Rousseau C, Olen O, Benchimol EI, Lynge E, et al. Sex-based Differences in Incidence of Inflammatory Bowel Diseases-Pooled Analysis of Population-based Studies from Western Countries. Gastroenterology. 2018.
5. Khalili H, Higuchi LM, Ananthakrishnan AN, Richter JM, Feskanich D, Fuchs CS, et al. Oral contraceptives, reproductive factors and risk of inflammatory bowel disease. Gut. 2013;62(8):1153-9.
6. De Simone V, Matteoli G. Estrogen-Mediated Effects Underlie Gender Bias in Inflammatory Bowel Disease. Cell Mol Gastroenterol Hepatol. 2018;5(4):638-9 e1.
7. Tiratterra E, Franco P, Porru E, Katsanos KH, Christodoulou DK, Roda G. Role of bile acids in inflammatory bowel disease. Annals of gastroenterology. 2018;31(3):266-72.
8. Choi CH, Song ID, Kim YH, Koo JS, Kim YS, Kim JS, et al. Efficacy and Safety of Infliximab Therapy and Predictors of Response in Korean Patients with Crohn's Disease: A Nationwide, Multicenter Study. Yonsei Med J. 2016;57(6):1376-85.
9. Ordas I, Mould DR, Feagan BG, Sandborn WJ. Anti-TNF monoclonal antibodies in inflammatory bowel disease: pharmacokinetics-based dosing paradigms. Clinical pharmacology and therapeutics. 2012;91(4):635-46.

What is the purpose of the analysis being proposed? Please select all that apply.: 
Summary-level data meta-analysis:
Summary-level data meta-analysis uses only data from YODA Project
Participant-level data meta-analysis:
Participant-level data meta-analysis uses only data from YODA Project
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

Primary outcome (Aim 1): Endoscopic remission (CDEIS <3 (CD) or MMES <=1 (UC)
Secondary outcome (Aim 1): Biochemical remission (normalization of CRP or fecal calprotectin)

Primary outcome (Aim 2): HBI <5 (CD) or partial Mayo score <=1 (UC)

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

The main predictor variable will be gender (male, female), which is a categorical variable.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

Covariates (adjustment variables), to be defined at time of initiation of biologic (T0) unless otherwise specified: drug class, age at initial diagnosis, age at initiation of biologic, BMI, race/ethnicity, smoking history (duration and amount), disease duration, Montreal classification (behavior, location), presence of peri-anal disease (CD), severity at diagnosis, severity at initiation of biologic, prior IBD-related surgery (binary yes/no, and type of surgery), history of colorectal neoplasia, history of any cancer (other than non-melanomatous skin), prior IBD therapy exposure such as corticosteroids and immunomodulators (type, dose, duration). Medication exposure is defined as at least 30 days of use. These variables were selected as they are known to impact disease severity and response to treatment.

Statistical Analysis Plan: 

We will compile summary statistics for the pooled data as well as each study individually, and present both the grouped summary data and stratified by sex. Chi square and Student’s t-test will be used to determine significance for categorical and continuous variables, respectively. We will pool the raw primary data of studies meeting inclusion criteria (defined above) and determine study estimates for the total pooled group and stratified by gender. We will perform multivariable analyses and adjust for co-variates that are associated with the outcome of interest with p <0.10. We will pool survival data by using logistic regression. We will test for interactions and effect modifiers such as age, disease severity, prior medications, surgery etc. Lastly, we will perform sensitivity analyses and meta-regression to determine sources of heterogeneity.

There are no prior data on expected magnitude of differences in therapeutic response based on gender. However, we expect that an at least 15% difference in response between genders is would be clinically relevant. Assuming a conservative response rate to biologics of 60%, a sample of 350 patients would be required to detect a clinically meaningful difference.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/156">NCT00036439 - C0168T37 - A Randomized, Placebo-controlled, Double-blind Trial to Evaluate the Safety and Efficacy of Infliximab in Patients With Active Ulcerative Colitis</a></li><li><a href="/node/157">NCT00096655 - C0168T46 - A Randomized, Placebo-controlled, Double-blind Trial to Evaluate the Safety and Efficacy of Infliximab in Patients With Active Ulcerative Colitis</a></li><li><a href="/node/158">NCT00207675 - C0168T47 - A Randomized, Multicenter, Open-label Study to Evaluate the Safety and Efficacy of Anti-TNF a Chimeric Monoclonal Antibody (Infliximab, REMICADE) in Pediatric Subjects With Moderate to Severe CROHN'S Disease</a></li><li><a href="/node/159">NCT00094458 - C0168T67 - Multicenter, Randomized, Double-Blind, Active Controlled Trial Comparing REMICADE® (infliximab) and REMICADE plus Azathioprine to Azathioprine in the Treatment of Patients with Crohn’s Disease Naive to both Immunomodulators and Biologic Therapy (Study of Biologic and Immunomodulator Naive Patients in Crohn’s Disease)</a></li><li><a href="/node/160">NCT00336492 - C0168T72 - A Phase 3, Randomized, Open-label, Parallel-group, Multicenter Trial to Evaluate the Safety and Efficacy of Infliximab (REMICADE) in Pediatric Subjects With Moderately to Severely Active Ulcerative Colitis</a></li><li><a href="/node/166">NCT00487539 - C0524T17 - A Phase 2/3 Multicenter, Randomized, Placebo-controlled, Double blind Study to Evaluate the Safety and Efficacy of Golimumab Induction Therapy, Administered Subcutaneously, in Subjects with Moderately to Severely Active Ulcerative Colitis</a></li><li><a href="/node/353">NCT00207662 - C0168T21 - ACCENT I - A Randomized, Double-blind, Placebo-controlled Trial of Anti-TNFa Chimeric Monoclonal Antibody (Infliximab, Remicade) in the Long-term Treatment of Patients With Moderately to Severely Active Crohn's Disease</a></li><li><a href="/node/354">NCT00207766 - C0168T26 - ACCENT II - A Randomized, Double-blind, Placebo-controlled Trial of Anti-TNF Chimeric Monoclonal Antibody (Infliximab, Remicade) in the Long Term Treatment of Patients With Fistulizing CROHN'S Disease</a></li><li><a href="/node/355">NCT00004941 - C0168T20 - A Placebo-controlled, Repeated-dose Study of Anti-TNF Chimeric Monoclonal Antibody (cA2) in the Treatment of Patients with Enterocutaneous Fistulae as a Complication of Crohn’s Disease</a></li><li><a href="/node/455">NCT00537316 - P04807 - Efficacy & Safety of Infliximab Monotherapy Vs Combination Therapy Vs AZA Monotherapy in Ulcerative Colitis (Part 1) Maintenance Vs Intermittent Therapy for Maintaining Remission (Part 2)</a></li><li><a href="/node/755">NCT01551290 - CR018769 - A Phase 3, Multicenter, Randomized, Double-Blind, Placebo-Controlled Study Evaluating the Efficacy and Safety of Infliximab in Chinese Subjects With Active Ulcerative Colitis</a></li><li><a href="/node/984">NCT01190839 - REMICADECRD3001 - Prospective, Multicenter, Randomized, Double-Blind, Placebo-Controlled Trial Comparing REMICADE (Infliximab) and Placebo in the Prevention of Recurrence in Crohn's Disease Patients Undergoing Surgical Resection Who Are at Increased Risk of Recurrence</a></li><li><a href="/node/985">NCT00269854 - C0168T16 - A Placebo-Controlled, Dose-Ranging Study Followed by a Placebo-Controlled, Repeated-Dose Extension of Anti-TNF Chimeric Monoclonal Antibody (cA2) in the Treatment of Patients With Active Crohn's Disease</a></li><li><a href="/node/986">C0168T16 - Efficacy and safety of retreatment with anti-tumor necrosis factor antibody (infliximab) to maintain remission in Crohn's disease.</a></li><li><a href="/node/1129">NCT00771667 - C0743T26 - A Phase 2b, Multicenter, Randomized, Double-blind, Placebo-controlled, Parallel Group Study to Evaluate the Efficacy and Safety of Ustekinumab Therapy in Subjects With Moderately to Severely Active Crohn's Disease Previously Treated With TNF Antagonist Therapy</a></li><li><a href="/node/1133">NCT01369329 - CNTO1275CRD3001 - A Phase 3, Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter Study to Evaluate the Safety and Efficacy of Ustekinumab Induction Therapy in Subjects With Moderately to Severely Active Crohn's Disease Who Have Failed or Are Intolerant to TNF Antagonist Therapy (UNITI-1)</a></li><li><a href="/node/1134">NCT01369342 - CNTO1275CRD3002 - A Phase 3, Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter Study to Evaluate the Safety and Efficacy of Ustekinumab Induction Therapy in Subjects With Moderately to Severely Active Crohn's Disease (UNITI-2)</a></li><li><a href="/node/1286">NCT00488631 - C0524T18 - A Phase 3 Multicenter, Randomized, Placebo-controlled, Double-blind Study to Evaluate the Safety and Efficacy of Golimumab Maintenance Therapy, Administered Subcutaneously, in Subjects With Moderately to Severely Active Ulcerative Colitis</a></li><li><a href="/node/1361">NCT01369355 - CNTO1275CRD3003 - A Phase 3, Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter Study to Evaluate the Safety and Efficacy of Ustekinumab Maintenance Therapy in Subjects With Moderately to Severely Active Crohn's Disease</a></li><li><a href="/node/3256">NCT00488774 - C0524T16 - A Phase 2/3 Multicenter, Randomized, Placebo-controlled, Double-blind Study to Evaluate the Safety and Efficacy of Golimumab Induction Therapy, Administered Intravenously, in Subjects With Moderately to Severely Active Ulcerative Colitis</a></li><li><a href="/node/3381">NCT00265122 - C0379T07 - A Multicenter, Randomized, Phase 2a Study of Human Monoclonal Antibody to IL-12p40 (CNTO 1275) in Subjects With Moderately to Severely Active Crohn's Disease</a></li><li><a href="/node/3526">NCT01863771 - CNTO148UCO3001 - A Safety and Effectiveness Study of Golimumab in Japanese Patients With Moderately to Severely Active Ulcerative Colitis</a></li><li><a href="/node/3531">NCT01988961 - CNTO148UCO2001 - A Study to Evaluate the Accuracy of a Subset of the Length-109 Probe Set Panel (a Genetic Test) in Predicting Response to Golimumab in Participants With Moderately to Severely Active Ulcerative Colitis</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2019

2018-3476

Project Title: 
Efficacy of Crohn’s Disease Treatment Stratified by Disease Phenotype
Specific Aims of the Project: 

Hypothesis: This study is an estimation study rather than one that aims to perform specific statistical hypothesis testing. However, as previously mentioned, the scientific hypothesis that motivates this study is that currently approved Crohn's treatments will differ in efficacy when assessed within specific subgroups.

Objective: To optimally position current therapy for Crohn’s disease by quantifying treatment efficacy stratified by disease phenotype, including anatomic location, behavior, and prior medication use (including a history of anti-Tumor Necrosis Factor-alpha failure)

Aims: We plan to quantify treatment efficacy by stratification along the lines of disease phenotype, including anatomic location, behavior (e.g. structuring, penetrating), and history of prior medication failure.

A secondary, exploratory goal of this work would be to perform comparative effectiveness (e.g. ranking of efficacy for each given subgroup). Network meta-analysis may be more appropriate for this objective, but may be hampered by limited trial number and patient numbers. While we plan to explore the potential of this methodology to yield useful results, achieving the first goal alone would represent a more than satisfactory outcome of this study.

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation
Associated Trial(s): 

Application Status

Ongoing
Scientific Abstract: 

Background: Crohn's Disease is a heterogenous disorder encompassing multiple distinct clinical phenotypes which arise from different biological pathways. Clinical studies and experience suggest that the efficacy of different agents varies by phenotype including anatomical location, disease behavior, and prior medication failure. Although this data is commonly collected in clinical trials, it has not been uniformly analyzed or published. As such, clinicians are left to select between treatments based on best guess of efficacy rather than high-quality evidence, leading to suboptimal treatment, excess therapeutic risk, and increased healthcare costs.

Objective: To optimally position current therapy for Crohn’s disease by quantifying treatment efficacy stratified by disease phenotype, including anatomic location, behavior, and prior medication use.

Study Design: Meta-analysis of randomized placebo-controlled clinical trials of therapeutic efficacy for Crohn’s disease

Participants: Adult patients over the age of 18 with active Crohn’s disease who received either placebo or molecularly-targeted therapy during trials of treatment induction or maintenance

Main Outcome Measure: Efficacy as measured by clinical response per study protocol, stratified by disease location, behavior, and history of prior medication use including anti-Tumor Necrosis Factor alpha failure

Statistical Analysis: Mixed-effects meta-analysis to assess therapeutic efficacy across phenotypic strata. Covariate significance testing and model sensitivity analysis will be performed.

Brief Project Background and Statement of Project Significance: 

Crohn’s Disease (CD) is an idiopathic and morbid syndrome of gastrointestinal inflammation with rising global incidence[1] but lacking curative or preventative strategies. It is a heterogenous disease entity that encompasses multiple distinct clinical phenotypes which arise from different biological pathways. Although treatment options for CD historically have been limited, recent decades have seen the advent of novel agents with well-defined molecular targets. At the present time, patient response to any of these treatments remains largely unpredictable. Therefore, the choice of treatment largely lies in other factors such as side-effect profile, cost, and convenience rather than of predicted efficacy.

Clinical experience, limited clinical trials[2], and preclinical models[3] all suggest that treatment response varies by disease phenotype. Although disease phenotypic classification – most commonly the Montreal Classification[4] -- is collected on an individual patient level in all modern trials, it has gone without dedicated subgroup analysis in most of the pivotal trials that led to FDA approval.

As a result, clinicians are presently unequipped to make high-confidence treatment recommendations to their patients who encompass the full phenotypic spectrum of Crohn’s disease. This therapeutic imprecision invariably leads to suboptimal disease control, excess exposure to medication-related risks, and increased healthcare costs.

Therefore, we propose to meta-analyze pre-existing, high-quality, placebo-controlled clinical trial data in order to prioritize FDA-approved therapeutic candidates by disease phenotype and help advance precision medicine for this morbid disease.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

The source of the data will be from randomized, placebo-controlled, phase 2-4 clinical trials of biologics (anti-Tumor Necrosis Factor alpha, anti-alpha4 beta7-Integrin, and anti-IL12/23) approved for the treatment of Crohn's Disease. We have identified a candidate list of trials by comprehensive search of clinicaltrials.gov. In this application we are specifically requesting the pharmacological trial data corresponding those sponsors partnered with the YODA platform; other pharmacological trial data is simultaneously being requested from other platforms (e.g. CSDR, Vivli).

Inclusion criteria includes completed, randomized, placebo-controlled, phase 2-4 studies of efficacy in adults over the age of 18. Exclusion criteria includes poor study enrollment, premature trial termination, and studies/patients receiving non-FDA-approved doses or routes of administration.

Narrative Summary: 

Crohn's Disease is a heterogenous disorder encompassing distinct clinical phenotypes which arise from different biological pathways. Preclinical and clinical studies suggest that the efficacy of different agents does vary by disease phenotype, including anatomical location. Although these data are commonly collected in clinical trials, they have not been uniformly analyzed by strata nor meta-analyzed across studies by phenotype. As such, clinicians are left to recommend treatments based on best guess of efficacy rather than high-quality evidence. Therefore, we propose to meta-analyze this existing trial data as an important first step towards realizing precision medicine for Crohn's Disease.

Project Timeline: 

We will initiate analysis immediately upon receipt of the data. We will spend the first 3-6 months exploring the structure of the data and performing data cleaning and harmonization. We will spend the subsequent 3 months performing data analysis, visualization and interpretation. We anticipate requiring another 1-2 months to prepare a manuscript for submission. Overall, we anticipate an 8-12 month analytic period prior to journal submission.

Dissemination Plan: 

We suspect that this analysis will have broad interest within the gastroenterology community. When the work is complete we will submit abstracts for presentation at national gastroenterology meetings as well as to gastroenterology journals with a wide readership base. Specific target journals would be Gastroenterology, the American Journal of Gastroenterology, and Inflammatory Bowel Diseases.

Bibliography: 

1. Ng, S. C. et al. Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies. Lancet (London, England) 390, 2769–2778 (2018).
2. Sands, B. E. et al. Infliximab Maintenance Therapy for Fistulizing Crohn’s Disease. N. Engl. J. Med. 350, 876–885 (2004).
3. Kiesler, P., Fuss, I. J. & Strober, W. Experimental Models of Inflammatory Bowel Diseases. Cell. Mol. Gastroenterol. Hepatol. 1, 154–170 (2015).
4. Satsangi, J., Silverberg, M. S., Vermeire, S. & Colombel, J.-F. The Montreal classification of inflammatory bowel disease: controversies, consensus, and implications. Gut 55, 749–53 (2006).

What is the purpose of the analysis being proposed? Please select all that apply.: 
News research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations
Preliminary research to be used as part of a grant proposal
Participant-level data meta-analysis:
Participant-level data meta-analysis will pool data from YODA Project with other additional data sources
Supplementary Material: 
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

The primary outcome measure will be treatment response as defined by the original study protocol. This will be dichotomized as a binary categorical variable for this study. The nearly all of the trials we have requested data from report this by an absolute or relative reduction in the Crohn's Disease Activity Index (e.g. “CDAI 150” defined as a CDAI score under 150, or "CR 70/100" defined by a 70 or 100 point reduction in CDAI score). We plan to pool these outcomes.

We are also requesting endoscopic scores and histologic results for each of the trials to the extent that they are available -- but if they are not we do not forsee any limitations to proceed with this analysis. We are requesting scores corresponding to the baseline time (prior to trial start) as well as at the time of the primary endpoint. We recognize that there are multiple endoscopic (e.g. CDEIS, SES-CD) and histologic (e.g. Geboes, Naini/Cortina, GHAS) scores; we will assess the available scores as they are made available to us and determine at that time whether or not the literature supports their comparability. These would be treated as binary categorical variables.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

The main predictor variable is receipt of the active drug vs placebo, also defined as a binary categorical variable.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

The phenotypic data we seek are disease location, behavior, age of onset, and the presence of perianal disease as defined by the Montreal classification, history of prior inflammatory-bowel disease medications (including antiTNF-alpha) as well as reasons for treatment failure if available (e.g. primary or secondary loss of response, unacceptable side effects), concurrent medications at baseline (e.g. immunosuppressants, glucocorticoids, aminosalicylates, antibiotics), the presence of comorbid extraintestinal manifestations, demographics (age, gender, race/ethnicity, body mass index (or if unavailable, weight), duration of disease), smoking status at the time of the trial, history of prior intestinal surgery, history of prior C Difficile infection, and baseline inflammatory biomarkers (C-Reactive Protein, Fecal Calprotectin, Erythrocyte Sedimentation Rate, and Albumin as available).

The clinical phenotypes listed above would be identified on the basis of what was documented by the physician/site investigator.

The above list of covariates adequately defines the desired phenotypes. Interactions will be explored in regression analysis but no unsupervised learning is planned.

Statistical Analysis Plan: 

Missing Values: For missing values corresponding to the primary endpoint we will perform non-responder imputation. For missing data corresponding to other variables we will perform group mean imputation. We will repeat the analysis using exclusion of missing data and perform sensitivity analysis to assess the robustness of our conclusions to these methods.

Statistical Procedure:
We will perform a mixed effects linear model meta-analysis and weight individual study effect sizes using the DerSimonian-Laird method. Treatment (active drug vs placebo), as well as aforementioned covariates (e.g. disease location, behavior, demographics (age, gender, race) and medication history including anti-tumor necrosis factor failure) will be treated as fixed effects. Individual studies will be treated as random effects. We will also explore network meta-analysis methods if the size of the available data will permit this.

Measures to Adjust for Multiplicity, Confounders, Heterogeneity:
We will assess study heterogeneity using Q and I-squared statistics. We will adjust for multiple testing using the Benjamin-Hochberg method to maintain a false discovery rate at the 0.05 level.

Sensitivity Analysis: We will assess the sensitivity of our model to intention-to-treat vs per-protocol analysis, inclusion vs exclusion of patients receiving non-FDA approved dosing, as well as statistical significance with and without multiple hypothesis correction. We will also test the sensitivity of our model using leave-one-out tests, and interactively perform the meta-analysis with n-1 studies to test if the result are influenced by one particular study.

QC Plan: We anticipate that many of the requested patient-level datasets have already undergone data-cleaning activities as a part of the study database lock process in the course of carrying out the trial. Our data-cleaning activities will focus on harmonizing the data between data-sets to facilitate meaningful meta-analytic cross-comparison and usage of the statistical software.

Programming Plans: We will be performing all data visualization and statistical analysis in the R programming environment. We will use the following statistical computing packages:
tidyverse
data.table
stringr
MICE
caret
lme4
metafor
plotly
DT
survminer
desctools
heatmap.2
lubridate
survival
RMarkdown
FrontierMatching
gemtc
netmeta

How did you learn about the YODA Project?: 
Software Used: 
RStudio
Associated Trials: 
<ol><li><a href="/node/159">NCT00094458 - C0168T67 - Multicenter, Randomized, Double-Blind, Active Controlled Trial Comparing REMICADE® (infliximab) and REMICADE plus Azathioprine to Azathioprine in the Treatment of Patients with Crohn’s Disease Naive to both Immunomodulators and Biologic Therapy (Study of Biologic and Immunomodulator Naive Patients in Crohn’s Disease)</a></li><li><a href="/node/353">NCT00207662 - C0168T21 - ACCENT I - A Randomized, Double-blind, Placebo-controlled Trial of Anti-TNFa Chimeric Monoclonal Antibody (Infliximab, Remicade) in the Long-term Treatment of Patients With Moderately to Severely Active Crohn's Disease</a></li><li><a href="/node/354">NCT00207766 - C0168T26 - ACCENT II - A Randomized, Double-blind, Placebo-controlled Trial of Anti-TNF Chimeric Monoclonal Antibody (Infliximab, Remicade) in the Long Term Treatment of Patients With Fistulizing CROHN'S Disease</a></li><li><a href="/node/984">NCT01190839 - REMICADECRD3001 - Prospective, Multicenter, Randomized, Double-Blind, Placebo-Controlled Trial Comparing REMICADE (Infliximab) and Placebo in the Prevention of Recurrence in Crohn's Disease Patients Undergoing Surgical Resection Who Are at Increased Risk of Recurrence</a></li><li><a href="/node/985">NCT00269854 - C0168T16 - A Placebo-Controlled, Dose-Ranging Study Followed by a Placebo-Controlled, Repeated-Dose Extension of Anti-TNF Chimeric Monoclonal Antibody (cA2) in the Treatment of Patients With Active Crohn's Disease</a></li><li><a href="/node/1129">NCT00771667 - C0743T26 - A Phase 2b, Multicenter, Randomized, Double-blind, Placebo-controlled, Parallel Group Study to Evaluate the Efficacy and Safety of Ustekinumab Therapy in Subjects With Moderately to Severely Active Crohn's Disease Previously Treated With TNF Antagonist Therapy</a></li><li><a href="/node/1133">NCT01369329 - CNTO1275CRD3001 - A Phase 3, Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter Study to Evaluate the Safety and Efficacy of Ustekinumab Induction Therapy in Subjects With Moderately to Severely Active Crohn's Disease Who Have Failed or Are Intolerant to TNF Antagonist Therapy (UNITI-1)</a></li><li><a href="/node/1134">NCT01369342 - CNTO1275CRD3002 - A Phase 3, Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter Study to Evaluate the Safety and Efficacy of Ustekinumab Induction Therapy in Subjects With Moderately to Severely Active Crohn's Disease (UNITI-2)</a></li><li><a href="/node/1361">NCT01369355 - CNTO1275CRD3003 - A Phase 3, Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter Study to Evaluate the Safety and Efficacy of Ustekinumab Maintenance Therapy in Subjects With Moderately to Severely Active Crohn's Disease</a></li><li><a href="/node/3381">NCT00265122 - C0379T07 - A Multicenter, Randomized, Phase 2a Study of Human Monoclonal Antibody to IL-12p40 (CNTO 1275) in Subjects With Moderately to Severely Active Crohn's Disease</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2019

2018-3321

Project Title: 
Evaluation of longitudinal serum M protein (or free light chain) to predict survival in patients with relapsed/refractory multiple myeloma
Specific Aims of the Project: 

The overall objective of this study is to assess whether the routine short-term measurement of M-protein (or FLC) could be used to predict long-term survival benefit for MM subjects under treatment.
Our specific aims are:
1) Develop longitudinal models for M-protein (or FLC) by utilizing data from MM clinical studies of NCT00574288, NCT01615029, NCT01985126, NCT02076009, NCT02136134 to derive TGI metrics;
2) develop parametric survival models by establishing the link between TGI metrics and OS (or PFS) and by identifying baseline patient prognostic factors for OS (or PFS).

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Application Status

Ongoing
Scientific Abstract: 

Background: Metrics derived by longitudinal tumor growth inhibition (TGI) modeling is an early predictive biomarker of long-term survival for several cancer types (Claret et al, 2009, Claret et al 2013, Bruno et al, 2014). Similar to tumor burden for solid tumors, serum M-protein (and/or involved FLC) levels are part of response criteria for Multiple Myeloma (MM) patients, and thus their dynamic change can predict the long-term clinical benefit (OS, PFS). Tumor growth inhibition (TGI) capture treatment effect and can predict OS (PFS) in drug-independent models. This framework can support Phase1/2 GO/NO GO decisions and the study design of Ph3 clinical trials, which use survival endpoints as primary endpoint.

Objective: Development of survival model linking OS (or PFS) to TGI metrics derived from longitudinal serum M-protein data in patients with relapsed/refractory MM and baseline prognostic factors.

Study Design and Participants: Participants will be patients with relapsed/refractory MM who have participated in the following clinical trials: NCT00574288, NCT01615029, NCT01985126, NCT02076009, NCT02136134. Part of data will be used for model development, remaining part will be used for external validation.

Main Outcome Measures: OS/PFS parametric model for MM

Statistical Analysis: A TGI model will be developed to describe the dynamic change of serum M-protein (or involved FLC) and translate it into TGI metrics. A parametric model for OS (PFS) will be developed to describe the survival time distribution as a function of TGI metrics and prognostic factors.

Brief Project Background and Statement of Project Significance: 

Model-based approaches has been valuable in drug development by integrating early clinical data to enhance learning and reduce the risk/cost of large confirmatory clinical trials. For oncology drug development, the decision-making in early phases often relies on the observation of short-term tumor shrinkage and achievement of ORR while regulatory decision for drug approval depends on long-term survival improvement (OS or PSF). Despite a qualitative association between the short-term endpoint and long-term endpoints, ORR estimates obtained from early phase clinical trials are imprecise (due to small sample size) and uninformative to support Phase 3 design of clinical trials with survival endpoints as primary objective. Therefore, the proposed Modeling & Simulation framework to improve the predictions of long-term survival benefit.

For ORR and early evaluation of antitumor activity, RECIST criteria is used for solid tumors, where tumor size is originally measured by imaging techniques on a continuous scale and transformed into 4 categories (complete response, partial response, stable disease, progressive disease). This categorization facilitates clinical interpretation of measured tumor size but has limitations as based on point estimate of categorized response. First, transforming a continuous variable into a categorical one results in loss of information. Second, RECIST assessment is categorized based on the best response at initial scan with subsequent scan only needed to confirm complete or partial response. Consequently, dynamic characteristics related to tumor progression, including natural tumor growth, treatment-related tumor shrinkage, and treatment-related resistance are generally not taken into account.

Instead of ORR, significant efforts have been made in the past decade to develop longitudinal tumor size models by leveraging all individual longitudinal tumor size information collected. These models, at a minimum, capture the competing rates of tumor size growth and shrinkage. Based on the models, several tumor growth inhibition (TGI) metrics: change in tumor size (CTS) at specified early time point, time to tumor growth (TTG), and tumor growth rate (KG) can be derived and linked to survival endpoints. This model-based approach has been applied to predict clinical response and OS in cancer patients for a variety of clinical settings (Claret et al, 2009, Claret et al 2013, Bruno et al, 2014).

In MM, M-protein (or involved FLC) are produced in excessive amounts by malignant plasma cells. The reduction of M-protein and normality of FLC ratio are part of the International Myeloma Working Group Uniform Response Criteria to assess response. Unlike tumor size for solid tumors, subject to a maximum of five target lesions per RECIST and based on imaging result assessed every 6-8 weeks, M-protein/FLC represents the total body burden and is measured in blood. Given the potentially better data quality and quantity with M-protein, we will evaluate the use of TGI modeling based on longitudinal M-protein (or FLC) data to predict long-term survival outcomes in MM patients as reported in previous analyses (Jonsson et al, 2015).

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

Patient-level data from Studies NCT00574288, NCT01615029, NCT01985126, NCT02076009 and NCT02136134. The following clinical and laboratory variables are requested:
1. Time posted the first treatment
2. Serum M protein over time
3. FLC over time
4. OS (1=evt, 0= censor)
5. PFS (1=evt, 0= censor)
6. Treatment arm
7. Study
8. Line of therapy
9. Refractory or relapsed status
10. Baseline ECOG
11. ISS
12. RISS
13. Prior therapy
14. Age
15. Baseline body weight
16. Baseline creatinine
17. Baseline LDH
18. Baseline Hemoglobin (g/L)
19. Baseline Albumin (g/L)

Narrative Summary: 

Longitudinal tumor size models were developed and model-based estimates of tumor growth inhibition (TGI) metrics were found predictive biomarkers of clinical outcome PFS (progression free survival) and OS (overall survival). These analyses were conducted in various cancer types with successful prediction of OS in independent Phase 3 studies based on Phase 2 TGI data (Claret et al, 2009, Claret et al 2013, Bruno et al, 2014). The link between TGI metrics and OS/PFS is assumed to be disease specific and treatment independent. The proposed study will evaluate the longitudinal dynamics of M-protein or free light chain (FLC) levels to derive TGI metrics and predict survival in multiple myeloma.

Project Timeline: 

The project is expected to be completed 1 year after data availability.
1. Completion of contract: July 2018
2. Obtain de-identified dataset: Aug-September 2018
3. Analysis and report submitted to YODA: March 2019
4. Circulating of abstract targeting ASH 2019 to YODA: April 2019
5. Circulating of paper to YODA targeting xxx Journal: July-Aug 2019

Dissemination Plan: 

The obtained model may be potentially used by the scientific community:
1. Characterize the link between (TGI) metrics as biomarkers to capture treatment effect and predict for OS/PFS benefit in "drug-independent" survival models
2. Simulate survival outcomes for any new therapy for which TGI metrics will be determined.

The resulting model(s) will be published and made available to the community.
1. Circulating of abstract targeting ASH 2019 to YODA: April 2019
2. Circulating of paper to YODA targeting xxx Journal: July-Aug 2019

Target journals are:
Clinical Pharmacology & Therapeutics (CPT)
CPT: Pharmacometrics & Systems Pharmacology (CPT: PSP)

Bibliography: 

Bruno R, Mercier F and Claret L. Evaluation of Tumor Size Response Metrics to Predict Survival in Oncology Clinical Trials. Clin Pharmacol Ther 95:386-393, 2014

Claret L, Girard P, Hoff PM, Van Cutsem E, Zuideveld KP, Jorga K, Fagerberg J, Bruno R. Model-based prediction of phase III overall survival in colorectal cancer on the basis of phase II tumor dynamics Journal of Clin Oncol, 27, 4103-4108, 2009.

Claret L, Gupta M, Han K, Joshi A, Sarapa N, He J, Powell B and Bruno R. Evaluation of Tumor-Size Response Metrics to Predict Overall Survival in Western and Chinese Patients With First-Line Metastatic Colorectal Cancer. Journal of Clin Oncol, 31, 2110-2114, 2013.

Jonsson F, Ou Y, Claret L, Siegel D, Jagannath S, Vij R, Badros A, Aggarwal S, Bruno R. A Tumor Growth Inhibition Model Based on M-Protein Levels in Subjects With Relapsed/Refractory Multiple Myeloma Following Single-Agent Carfilzomib Use. Clinical Pharmacology Therapeutics: Pharmacometry and System Pharmacology, 4, 711-719, 2015.

Stein WD, Gulley JL, Schlom J, Madan RA, Dahut W, Figg WD, et al. Tumor regression and growth rates determined in five intramural NCI prostate cancer trials: the growth rate constant as an indicator of therapeutic efficacy. Clin Cancer Res, 17(4), 907–917, 2011.

What is the purpose of the analysis being proposed? Please select all that apply.: 
News research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

OS (or PFS) parametric model for MM disease

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

TGI metrics derived from individual M-protein (or FLC) data over time.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

Baseline prognostic factors

Statistical Analysis Plan: 

The Modeling & Simulation framework employed here is based on: a drug-dependent TGI model and a drug independent (disease dependent) survival model. Treatment effect is captured by the TGI metrics which can be considered as biomarkers and known as very good predictors of survival outcomes.

TGI model
Longitudinal M-protein (or FLC) will be analyzed using non-linear mixed effects modeling in NONMEM. Two model structures will be tested:
1. Simplified TGI model by Claret et al 2013
2. Bi-exponential model by Stein et al, 2011
Those models will be used to derive metrics: tumor size ratio to baseline at week 8 (TS ratio, with model 1), Time to Growth (TTG, with model 1) and KG (growth rate constant, with model 2). There will be no formal further development of the TGI models because they are already established and only meant to derive TGI metrics but there could still be some model optimization (random effects, error model), candidate models will then be selected based on the minimum objection function (-2xloglikelyhood).

Survival model
1. TGI metrics will be merged with survival data (OS/PFS) i.e. survival time and information on death/progression events, censoring, potential baseline prognostic factors.
2. Part of the data, i.e. approximately 2/3, will be used for model development.
3. The impact of individual baseline prognostic factors and TGI metrics on OS/PFS will be explored using Kaplan-Meier and Cox regression analyses using survfit and coxph functions, respectively in R. The univariate Cox survival analysis (coxph function in R) will be performed to screen relevant prognostic factors prior to parametric modeling.
4. The survival analysis will be performed in R using a parametric survival modeling approach using the survreg function in R. The probability density function that best describes the observed survival times will be selected among normal, lognormal, Weibull, logistic, log-logistic, and exponential using the difference in Akaike information criterion (AIC) of the alternative models. A “full” model will be built by including all significant covariates screened in the Cox univariate analysis with a significance level of p < 0.05 per the log-likelihood ratio test where the difference in -2*log-likelihood (score) between alternative models follows a χ2 distribution. The score indicates the level of significance for the association between a covariate and OS ( or PFS): the higher the score, the more significantly this covariate is associated with OS (or PFS). Then a backward stepwise elimination will be carried out. At each elimination step, one covariate will be removed from the model. If the reduced model (without this removed covariate) becomes significantly worse (p < 0.01), the removed covariate stays in the model. The relative influence of each remaining covariate on the model will be re-evaluated by deleting it from the reduced model on an individual basis with a significance level of p < 0.01. The backward elimination will result in the final model, in which all covariates will be significant.
5. The predictive performance of the model will be assessed by posterior predictive checks.
6. The predictive performance will be further assessed by an external validation (similar to step 5) based on the remaining part of the data, i.e. approximately 1/3 of data.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/2196">NCT02076009 - 54767414MMY3003 - Phase 3 Study Comparing Daratumumab, Lenalidomide, and Dexamethasone (DRd) vs Lenalidomide and Dexamethasone (Rd) in Subjects With Relapsed or Refractory Multiple Myeloma</a></li><li><a href="/node/2201">NCT02136134 - 54767414MMY3004 - Phase 3 Study Comparing Daratumumab, Bortezomib and Dexamethasone (DVd) vs Bortezomib and Dexamethasone (Vd) in Subjects With Relapsed or Refractory Multiple Myeloma</a></li><li><a href="/node/2206">NCT01615029 - DARA-GEN503 - An Open Label, International, Multicenter, Dose Escalating Phase I/II Trial Investigating the Safety of Daratumumab in Combination With Lenalidomide and Dexamethasone in Patients With Relapsed or Relapsed and Refractory Multiple Myeloma</a></li><li><a href="/node/3001">NCT00574288 - 54767414GEN501 - Daratumumab (HuMax®-CD38) Safety Study in Multiple Myeloma - Open Label, Dose-escalation Followed by Open Label, Single-arm Study</a></li><li><a href="/node/3006">NCT01985126 - 54767414MMY2002 - An Open-label, Multicenter, Phase 2 Trial Investigating the Efficacy and Safety of Daratumumab in Subjects With Multiple Myeloma Who Have Received at Least 3 Prior Lines of Therapy (Including a Proteasome Inhibitor and IMiD) or Are Double Refractory to a Proteasome Inhibitor and an IMiD</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2019

2018-3021

Project Title: 
Variability in health-related quality of life following treatment with abiraterone acetate for metastatic castration-resistant prostate cancer
Specific Aims of the Project: 

The objectives of this study are: (1) to explore whether subgroups exist that exhibit differential response to treatment in terms of differential survival and differential effects on QoL; (2) the indirect effects of treatment on QoL domains via the effect of treatment on symptoms, particularly pain and fatigue. We anticipate that there will be subgroups of differential responders, which will be informed by their patterns of change in QoL outcomes. That is, patients who show worsening fatigue or pain early in the trial will be those who are more likely to have worse survival, and these worsening trajectories will be clinically important indications of survival. Further, based on our previous research in oncology, we anticipate that pain and fatigue will play important mediating roles between treatment, disease severity, and QoL. In particular, improvements in pain (BPI-SF) and fatigue (BFI) will result in improvements in Qol domains of the FACT-P, but that treatment effects will be indirect on QoL through their effects on pain and fatigue. The use of these two studies will allow us to explore the detailed influence of pain (using the BPI-SF in NCT00887198) and fatigue (using the BFI in NCT00638690) on QoL.

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Application Status

Incomplete Not Reviewed
Scientific Abstract: 

Background: Nearly all trials find that patient response to treatment is variable, both within and between patients. Traditional analyses compare group (treatment arm) differences, yet those differences may be overwhelmed by variability within each treatment arm. Moreover, there may indirect relationships between treatment, symptoms, and more distal patient-reported outcomes, resulting in attenuated or non-significant relationships between treatment and these outcomes.
Objectives: To explore whether (1) subgroups of patients show differential response to treatment and (2) there are indirect effects of treatment on patient-reported outcomes.
Study Design: post hoc exploratory analysis.
Participants: 1,195patients in NCT00638690 and 1,088 in NCT00887198
Main Outcome Measures: Functional Assessment in Cancer Therapy-Prostate (FACT-P), Brief Pain Inventory-Short Form (BPI-SF), Brief Fatigue Inventory (BFI)
Statistical Analysis: Growth mixture modeling to identify subgroups of differential responders to treatment and their characteristics; structural equation modeling to examine the indirect effects of treatment on patient-reported symptoms and quality of life outcomes.

Brief Project Background and Statement of Project Significance: 

Heterogeneity of treatment effects (HTEs) are common in clinical trials: Not all patients respond to treatment in the same way, and this may vary by treatment arm. In addition, when examining the effect of treatment on patient-reported quality of life (QoL), there may be many intervening/mediating variables between treatment, its effect on symptoms, such as nausea, pain, and fatigue, and downstream effects on functioning and QoL. Typical trial analyses examine mean differences between treatment arms; however, the variability around these means may overwhelm the differences between the groups, leading to conclusions of little or no difference between treatment arms. In fact, there may be identifiable subsets of patients who show very different treatment effects, and these may be tied to objective criteria, such as biomarkers, comorbidities, or prior treatments. In addition, simple mean comparisons between arms on QoL outcomes may not show differences because treatment may not have a direct effect on those QoL outcomes. The proposed analyses would use data from two YODA trials (NCT00887198 and NCT00638690) to explore within the variability of response to treatment to see if subsets of patients within treatment arms can be identified who are similar to one another in their response but are significantly different from other patients in their response. In addition, we propose examining how treatment affects symptoms, especially pain and fatigue, which then affect the QoL domains of the FACT-P. The investigators have examined these types of analyses in trials examining treatments for non-small-cell lung cancer and metastatic breast cancer (references attached). We are interested in using these two trials as they will allow us to explore the detailed effects of pain (NCT00887198) and fatigue (NCT00638690) on the QoL outcomes and inform us about differential survival. The trials are large enough that we can randomly subset each trial to allow for an "exploratory sample" and a "validation sample." Results will allow us to compare with our previous, consistent findings to see if similar causal processes are found in treatment of men with metastatic castration-resistant prostate cancer using somewhat different measures (the prior analyses used the EORTC QLQ-C30). In addition, there are implications of these results for clinical trials and clinical practice. Namely, the analyses of HTEs can aid in future trial design by identifying patients most likely to be responsive this type of treatment. Likewise, for clinical practice, understanding the extent to which QoL outcomes are affected by symptom change can be informative of treatment effectiveness. In addition, if a treatment results in certain AEs but overall survival is improved and QoL is not affected adversely, this is an important finding for patients and clinicians.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

We are requesting patient-level data for all 1,195patients in NCT00638690 and 1,088 patients in NCT00887198. Data from both trials would be randomly spilt into two sample for each trial: one random half would be the exploratory sample; the second random half would be the validation sample. We don't anticipate excluding any patients that are included in the trial datasets, unless they do not have any available patient-reported outcomes (PRO) data.

Narrative Summary: 

Variability in treatment response is common. The result is little or no statistical difference between treatment arms where some patients who show very high response while others show very little response. Mean response masks these differences. In addition, some effects of treatment on health-related quality of life may not be direct but may involve a more complex "causal cascade" from symptoms to functional domains to overall quality of life. Thus, treatment may significantly affect more distal outcomes, but not directly. The proposed analyses would explore variability in patient-reported outcomes by treatment and whether patient-reported symptoms indirectly affect domains of well-being.

Project Timeline: 

We hope for a project start date of 1 September 2018. It will take approximately 2 weeks following receipt of data to familiarize ourselves with the data and format the data into a shape that we can analyze. It will take 24 weeks to analyze the data, 12 weeks for each trial. After data analyses are complete, we plan to write four manuscripts, two for each trial (one for the SEPM analyses and one for the GMM/PMM analyses). We anticipate that it will take 4 weeks to write each manuscript. Each manuscript will undergo professional editing by one of the RTI Health Solutions editors to comply with the publication requirements of the selected journal(s). The editing and responding to editorial comments/changes will take an additional week per manuscript. At this point, results can be submitted to the YODA Project as well as being submitted to the selected journal(s). Once manuscripts are drafted, we plan to submit conference abstracts to ASCO (Am. Society of Clinical Oncology) and ISPOR (International Society for Pharmacoeconomics and Outcomes Research). The total timeline is anticipated to take approximately 48 weeks.

Dissemination Plan: 

We anticipate writing 4 manuscripts, 2 for each clinical trial. One manuscript for each trial will be focused on the results of the SEPM analyses; one for each trial will be based on the results of the GMM/PMM analyses. There are several potential journals that we can submit these manuscripts to: Value in Health (the ISPOR journal); Journal of Clinical Oncology (JCO; the ASCO journal); Statistics in Medicine; and BMC Cancer. The intended audiences include outcomes researchers and clinical oncologists. The goal would be to demonstrate that methods exist that allow us to examine relationships between treatment, symptoms, and QoL outcomes that reflects the more complex relationships among these variables and provide empirical evidence to support clinical practice. In addition, we would plan to submit abstracts to two primary types of conferences, two in outcomes research and two in oncology: ISPOR international conference in North America and the ISPOR European conference; the ASCO annual meeting in North America; and the ESMO Congress (European Society for Medical Oncology) in Europe.

Bibliography: 

Boye ME, Houghton K, Stull DE, Ainsworth C, Price GL. Estimating the Effects of Patient-reported Outcome (PRO) Diarrhea and Pain Measures on PRO Fatigue: Data Analysis from a Phase 2 Study of Abemaciclib Monotherapy, A CDK4 and CDK6 Inhibitor, in Patients with HR+/HER2- Breast Cancer, after Chemotherapy for Metastatic Disease: MONARCH 1. Presented at the American Society of Clinical Oncology, Chicago, IL; June 2-6, 2017.

Boye ME, Houghton K, Stull DE. Rethinking what missing patient-reported outcome (PRO) data can teach us about outcomes and survival in oncology clinical trials. Presented at the ISPOR 19th Annual European Congress; November 1, 2016. Vienna, Austria.

Stull DE, Houghton K, Ainsworth C, Brown J, Bowman L, Boye ME. "Causal cascade" among outcomes in non-small cell lung cancer: assessing the direct and indirect effects of symptoms on health-related quality of life (HRQL) outcomes. Poster presented at the ISPOR 19th Annual European Congress; November 2, 2016. Vienna, Austria.

Boye ME, Stull DE, Ainsworth C. Changing our perspective on patient-reported outcomes in oncology: it's more complex than traditionally conceptualized or analyzed. Presented at the ISPOR 19th Annual European Congress; November 1, 2016. Vienna, Austria.

Houghton K, Boye M, Bowman L, Brown J, Stull D. Longitudinal modeling of informatively censored patient-reported outcomes data in oncology: application to
a Phase III clinical trial of non-small cell lung cancer. Presentation at the ISPOR 21st Annual International Meeting Washington DC, USA; May, 2016.

Stull DE, Houghton K. Identifying Differential Responders and Their Characteristics in Clinical Trials: Innovative Methods for Analyzing Longitudinal Data. Value in Health. 2013; 16(1): 164-176.

Stull DE, Wiklund I, Gale R, Capkun-Niggli G, Houghton K, Jones P. Application of latent growth and growth mixture modeling to identify and characterize differential responders to treatment for COPD. Contemporary Clinical Trials. 2011; 32(6):818-28.

Stull DE, Kosloski K, Kercher K. A comparison of patient and clinician assessments of functional ability in predicting number of hospitalizations for older patients with left ventricular dysfunction. Health Outcomes Res Med. 2011 Feb;2(1):e15-e25.

Stull DE, Wyrwich K. Uncovering Heterogeneity in Clinical Trials and Observational Studies. ISPOR Connections. 2010, 16(4): 4-6.

Stull DE, Kercher K, and Kosloski K. (1996). Physical health and long-term care: A multidimensional approach. American Behavioral Scientist, 39:317-324.

What is the purpose of the analysis being proposed? Please select all that apply.: 
News research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

The main outcome measures: FACT-P and the BPI-SF for trial NCT00887198 (to measure pain and its interference on patient's lives in greater detail than the FACT-P will allow), and the FACT-P and BFI for trial NCT00638690 (to measure fatigue and its interference on patient's lives in greater detail than the FACT-P will allow). Our previous research indicated that pain and fatigue play important roles in determining QoL for patients with lung and breast cancer. However, the FACT-P only has one global pain question and three pain-related questions; it has only one global fatigue question (lack of energy). Thus, the pain and fatigue items would be supplemented by using the BPI-SF and the BFI, respectively.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

We will explore the effects of treatment (abiraterone acetate plus prednisone vs placebo plus prednisone in NCT00887198; and abiraterone acetate versus placebo in NCT00638690) on pain and fatigue (and Other Concerns in the FACT-P), and treatment and pain on fatigue (and Other Concerns in the FACT-P) on the QoL domains of the FACT-P. In addition, we will explore whether these variables show differential changes over the course of the trials, and if these are related to survival. We anticipate that patients who show worsening pain and fatigue will show poorer survival. We will examine whether this pattern is different for each treatment arm.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

If available in each trial, we will explore the effects of age, comorbidities, time since diagnosis, prior therapies, stage of disease, and medical resource utilization.

Statistical Analysis Plan: 

We will randomly subset each trial; one random half will be the exploratory sample; the other random half will be the validation sample. NCT00887198 will allow us to examine the detailed effects of pain on QoL; NCT00638690 will allow us to examine the detailed effects of fatigue on these outcomes. The primary analyses will involve structural equation modeling (SEM) using Mplus version 8. Two variants of SEM will be conducted: (1) structural equation path modeling (SEPM), and (2) growth mixture models (GMMs) with a pattern mixture model (PMMs) approach, for identifying different trajectories of change in PROs based on patterns of missing (e.g., those with all assessments vs those with all but 2 assessments vs those with only a few assessments). Step 1: Conduct SEPMs to test whether pain and fatigue play a central role in the relationship between treatment, selected symptoms, such as "I have good appetite," "I have trouble moving my bowels," pain, fatigue, and the QoL domains of the FACT-P. In addition, selected items from the Physical Well-Being domain of the FACT-P (e.g., "I have nausea," "I have pain," "I have lack of energy") will be analyzed as symptoms of the disease or its treatment rather than as items of Physical Well-Being. Our expectation, based on previous research, is that some of the different symptoms (e.g., nausea, appetite, pain) will affect fatigue which will affect QoL. This may vary by treatment, depending on differential efficacy and side effect profile. Using information from this "causal cascade" amongst symptoms and QoL, we will select the key patient-reported symptoms for the next analytic step. Step 2: GMMs/PMMs will use data for each patient for all available time points to help identify any subgroups of differential responders to treatment based on their patterns of change in, say, pain and fatigue (including dummy variables for missing PRO data at each time point, on which the subgroup identifier is regressed). Using this information, we will then examine the survival of these different subgroups. We anticipate that those patients who show greater worsening of pain or fatigue early in the trial are likely to be those patients who drop out of the trial sooner (i.e., have poorer survival). Thus, the trajectories of change in such things as pain and fatigue will be informative of those who are less likely to survive. These findings will have important clinical implications.

NOTE, for the planned analyses:

(1) the use of patient-level data, which allows us to identify different patterns of missing (e.g., data available for all timepoints vs data for all but 1 timepoint vs data for all but 2 timepoints, etc) and then jointly model the different patterns of missing, the slopes of change in the scores for the available data timepoints, and survival data. This will let us know if those patients who drop out sooner (shorter survial) are also those whith greater worsening scores on, say, fatigue or pain compared with those patients who are in for the entire trial.

(2) all planned analyses can be performed using the latent variable analysis software, Mplus. Standard statistical packages (e.g., SAS or Stata) cannot do the analyses proposed in this application. The statistical package R has latent variable capabilities, but neither of the researchers on this project has experience with R, and it would take many months to become sufficiently familiar with R to determine if the proposed analyses can be performed with this package. To conduct these analyses, we need to use patient-level data locally with the Mplus software. RTI-Health Solutions has secure servers and we work with proprietary, patient-level clinical trial data all the time, so we are aware of the security needs for these data. Alternatively, YODA would need to implement Mplus as part of their analytic capabilities for us to conduct this research.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/304">NCT00638690 - COU-AA-301 - A Phase 3, Randomized, Double-Blind, Placebo-Controlled Study of Abiraterone Acetate (CB7630) Plus Prednisone in Patients With Metastatic Castration-Resistant Prostate Cancer Who Have Failed Docetaxel-Based Chemotherapy</a></li><li><a href="/node/1115">NCT00887198 - COU-AA-302 - A Phase 3, Randomized, Double-blind, Placebo-Controlled Study of Abiraterone Acetate (CB7630) Plus Prednisone in Asymptomatic or Mildly Symptomatic Patients With Metastatic Castration-Resistant Prostate Cancer</a></li></ol>
Make Publicly Available : 

2018-3011

Project Title: 
Clinical Significance and Factors Associated with PSA Progression in mCRPC Patients Treated with Abiraterone
Specific Aims of the Project: 

OVERALL AIMS:
To determine:
(1) Incidence, prognostic significance and factors associated with a PSA progression in abiraterone + prednisone and placebo + prednisone treated patients with mCRPC.
(2) Value of PSA-only progression in mCRPC abiraterone-treated patients.

SPECIFIC ENDPOINTS:
Primary Endpoint:
- Association between PSA progression and overall survival.

Secondary Endpoints:
- To evaluate, for each of the PSA progression categories (primary PSA progression, secondary PSA progression, PSA only progression):
• Incidence
• Association with OS, rPFS, cPFS, time to QoL deterioration
• Association with baseline clinical factors.
- Additionally, to evaluate for PSA only progression:
• Time to radiographic/clinical progression or death
• Time to quality of life / patient reported outcome (FACT-P) deterioration
• Baseline clinical factors associated with OS, rPFS, cPFS in patients with PSA-only progression

Exploratory Endpoints:
- To determine the rate of PSA-flare (PSA progression at 12 weeks, with a subsequent PSA response).
- To determine the rate of patients experiencing radiographic or clinical progression without a PSA progression.
- To explore Prentice Criteria for surrogacy for PSA progression.

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Application Status

Ongoing
Scientific Abstract: 

Background: abiraterone acetate (AA) has been shown to prolong survival in two large randomized trials in mCRPC patients. In both trials, patients experiencing PSA progression (PSAprog) exclusively were allowed to continue on treatment. Rates of PSA only progression in abiraterone-treated patients have not been reported, and its prognostic significance remains unknown.

Objective: to evaluate the incidence, prognostic significance and factors associated with PSAprog in mCRPC patients treated with AA/placebo + prednisone.

Study Design: retrospective cohort study.

Participants: mCRPC patients treated in the COU-AA-301 and COU-AA-302 trials, with a baseline and at least one post-treatment PSA value.

Main Outcome Measures: Overall survival.

Statistical Analysis: The proportion of patients experiencing primary, secondary PSAprog and PSA-only progression will be calculated. Uni- and multivariable Cox proportional hazards models will be used to evaluate the association of PSAprog and OS, rPFS, cPFS and time to QoL deterioration. Time from PSAprog only to radiographic/clinical progression or death will be calculated. Uni- and multivariable Cox proportional hazards (PH) models will be used to evaluate factors associated with time to rPFS / cPFS in patients with PSAprog only. Known prognostic clinical factors will be included as covariates in each of the Cox-PH models. The performance of the models will be evaluated by calculating the c-indices. Analyses will be performed in all subjects, and separately in the abiraterone and placebo-treated cohorts.

Brief Project Background and Statement of Project Significance: 

Although an isolated PSA progression (without radiographic or clinical progression) is accepted as an entry criterion for clinical trials, PCWG3 guidelines do not mandate treatment discontinuation with PSA progression. Similarly, clinical guidelines do not recommend treatment discontinuation based on PSA progression exclusively in daily clinical practice;4,5 generally, continuation until the occurrence of radiographic or clinical progression is recommended. These recommendations are not based on prospective data evaluating the value of a rising PSA on treatment. Furthermore, factors influencing the time between PSA and radiographic/clinical progression are largely unknown.

Most analyses have focused on the survival advantage of patients experiencing a PSA decline on treatment.6,7 A number of different studies have established a favourable outcome for patients experiencing 30% or 50% PSA declines. PSA response, however, has failed to meet the Prentice Criteria and has therefore not qualified as a surrogate for overall survival.8

The prognostic significance of a rise in PSA, either as a primary rise (at 12 weeks after treatment initiation) or as a secondary progression (after a previous PSA decline) is less well studied. PSA progression at a landmark 3 or 7-month time point has been associated with a worse outcome in chemotherapy-treated patients in two randomized clinical trials.9 In an single-centre study of abiraterone-treated patients, PSA progression as early as 4 weeks after treatment initiation was associated with an increased risk of death in both the pre- (HR 2.43 [95%CI:1.24-4.76]; p=0.009) and post-chemotherapy (HR 1.85 [95%CI: 1.17-2.92]; p=0.008) settings.7 A shorter PSA doubling time has also been associated with worse outcomes in abiraterone-treated patients in the COU-AA-301 and COU-AA-302 trials.10

To our knowledge, no study has yet reported rates of primary PSA progression, or analysed the significance of an exclusive PSA progression (without radiographic) COU-AA-301 and COU-AA-302 trials. Specific data on the natural history of patients experiencing PSA-only progression are also lacking.

We aim to determine:
(a) The incidence, prognostic significance and factors associated with a PSA-only progression, either primary (at 12 weeks after treatment initiation) or secondary (after PSA response, or a lack of progression at 12 weeks) in mCRPC patients treated with abiraterone + prednisone or placebo + prednisone.
(b) Time elapsed between PSA progression and clinical/radiographic progression in mCRPC patients treated with abiraterone + prednisone or placebo + prednisone. Clinical factors associated with time between PSA progression and radiographic/clinical progression.

We anticipate our results will help clinicians perform better informed decision-making in patients with PSA progression on abiraterone, by enabling clinicians to identify patients at a high risk of clinical or radiographic progression, that may benefit from a switch of treatment to a different treatment agent before clinical deterioration ensues.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

Data Source: COU-AA-301 and COU-AA-302 datasets.

Inclusion Criteria:
Patients treated with abiraterone + prednisone or placebo + prednisone in the COU-AA-301 and COU-AA-302 trials.
Survival >= 12 weeks.
Baseline PSA value and at least one post-treatment PSA value available.

Narrative Summary: 

Asessment of treatment efficacy in metastatic castration-resistant prostate cancer is commonly based on a composite endpoint including PSA, radiographic and clinical progression.1 Despite the use of PSA response as a secondary endpoint in most trials, the clinical significance of a PSA progression is unclear, especially in the absence of clinical/radiographic progression. Most trials did not mandate treatment discontinuation in this scenario.
We aim to evaluate the clinical significance of PSA progression in abiraterone-treated patients in the COU-AA-301/302 trials,2,3 in order to identify patients at a high risk of clinical or radiographic progression before clinical deterioration ensues.

Project Timeline: 

- Project submission: August 2018
- Contract: September 2018
- Analysis: September - November 2018
- Abstract Submission (ASCO GU 2019): October 2018
- Paper Draft circulation: January-February 2019
- Paper Submission: April-May 2019

Dissemination Plan: 

- Abstract presentation in ASCO GU 2019
- Submission of manuscript first-quartile oncology journals: Annals of Oncology, European Urology, Clinical Cancer Research

Bibliography: 

1. Scher HI, Morris MJ, Stadler WM, et al. Trial Design and Objectives for Castration-Resistant Prostate Cancer: Updated Recommendations From the Prostate Cancer Clinical Trials Working Group 3. J Clin Oncol. 2016;34(12):1402-1418. doi:10.1200/JCO.2015.64.2702.
2. de Bono JS, Logothetis CJ, Molina A, et al. Abiraterone and increased survival in metastatic prostate cancer. N Engl J Med. 2011;364(21):1995-2005. doi:10.1056/NEJMoa1014618.
3. Ryan CJ, Smith MR, de Bono JS, et al. Abiraterone in metastatic prostate cancer without previous chemotherapy. N Engl J Med. 2013;368(2):138-148. doi:10.1056/NEJMoa1209096.
4. Cornford P, Bellmunt J, Bolla M, et al. EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part II: Treatment of Relapsing, Metastatic, and Castration-Resistant Prostate Cancer. Eur Urol. 2017;71(4):630-642. doi:10.1016/j.eururo.2016.08.002.
5. Gillessen S, Attard G, Beer TM, et al. Management of Patients with Advanced Prostate Cancer: The Report of the Advanced Prostate Cancer Consensus Conference APCCC 2017. Eur Urol. 2017:1-34. doi:10.1016/j.eururo.2017.06.002.
6. Berthold DR, Pond GR, Roessner M, de Wit R, Eisenberger M, Tannock AIF. Treatment of hormone-refractory prostate cancer with docetaxel or mitoxantrone: relationships between prostate-specific antigen, pain, and quality of life response and survival in the TAX-327 study. Clin cancer Res. 2008;14(9):2763-2767.
7. Rescigno P, Lorente D, Bianchini D, et al. Prostate-specific Antigen Decline After 4 Weeks of Treatment with Abiraterone Acetate and Overall Survival in Patients with Metastatic Castration-resistant Prostate Cancer. Eur Urol. 2016;70(5):724-731. doi:10.1016/j.eururo.2016.02.055.
8. Halabi S, Armstrong AJ, Sartor O, et al. Prostate-specific antigen changes as surrogate for overall survival in men with metastatic castration-resistant prostate cancer treated with second-line chemotherapy. J Clin Oncol. 2013;31(31):3944-3950. doi:10.1200/JCO.2013.50.3201.
9. Hussain M, Goldman B, Tangen C, et al. Prostate-specific antigen progression predicts overall survival in patients with metastatic prostate cancer: data from Southwest Oncology Group Trials 9346 (Intergroup Study 0162) and 9916. J Clin Oncol. 2009;27(15):2450-2456. doi:10.1200/JCO.2008.19.9810.
10. Xu XS, Ryan CJ, Stuyckens K, et al. Correlation between prostate-specific antigen kinetics and overall survival in abiraterone acetate-treated castration-resistant prostate cancer patients. Clin Cancer Res. 2015;21(14):3170-3177. doi:10.1158/1078-0432.CCR-14-1549.
11. Basch E, Autio K, Ryan CJ, et al. Abiraterone acetate plus prednisone versus prednisone alone in chemotherapy-naive men with metastatic castration-resistant prostate cancer: patient-reported outcome results of a randomised phase 3 trial. Lancet Oncol. 2013;14(12):1193-1199. doi:10.1016/S1470-2045(13)70424-8.

What is the purpose of the analysis being proposed? Please select all that apply.: 
Research on clinical prediction or risk prediction
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

MAIN OUTCOME MEASURE
Overall survival, defined as the time (months) from PSA progression to death.

SECONDARY OUTCOME MEASURES
- Radiographic progression-free survival (rPFS), which will be defined as the time from PSA progression to radiographic progression or death
- Clinical progression-free survival (cPFS) or death, which will be defined as the time from PSA progression to clinical progression, in months.
- Time to quality of life deterioration will be defined as the time from PSA progression to clinically significant FACT-P or BPI-SF progression.

Radiographic and clinical progression will be defined according to the definitions established in each of the corresponding trial protocols (COU-AA-301, COU-AA-302).2,3

Quality of Life / Patient Reported Outcomes: quality of life will be quantified according to BPI-SF and FACT-P questionnaire results. Thresholds for clinically significant BPI-SF progression and/or FACT-P progression will be defined per criteria from the COU-AA-302 trial.11

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

PSA progression will be defined as a ≥25% and ≥ 2 ng/mL increase from baseline (if no initial PSA decline is observed), or a ≥25% and ≥ 2 ng/mL increase above the nadir (if an initial PSA decline is observed), confirmed by a second value ≥ 3 weeks later. Time to PSA progression will be defined as the time from treatment initiation to PSA progression.

Three categories for PSA progression will be defined:
- Primary PSA progression: PSA progression at 12 weeks
- Secondary PSA progression: PSA progression after a previous response or stable PSA at 12 weeks
- PSA-only progression (primary/secondary): PSA progression with no documented radiographic or clinical progression

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

Baseline variables:
- Treatment arm: categorical
- Ethnicity: categorical
- Age, height, weight: continuous
- Type of disease progression at baseline: categorical
- Time from LHRH treatment to trial treatment initiation
- Presence of bone, node, liver, other visceral metastases: yes/no
- Gleason Score: ordinal
- Prior surgery or radiation therapy to primary: yes/no

Baseline and at post-baseline time-points:
- Hemoglobin, albumin, alkaline phosphatase, LDH, PSA: continuous.
- ECOG PS: ordinal (0-4)
- BPI-SF score, analgesic score (continuous)
- FACT-P score (continuous)
- Post-baseline radiographic evaluation (BS/CT scan): categorical

Statistical Analysis Plan: 

- A descriptive analysis of endpoints and baseline covariates will be performed. Results will be presented as the median and interquartile range (IQR) for continuous variables and as number and percentage frequency for categorical variables.
- Logistic regression models will be used to determine the association between PSA progression and the different baseline variables. Odds ratio estimates and 95% confidence intervals will be calculated.
- The Kaplan-Meier method will be used to estimate median survival times (OS, rPFS, cPFS) and 95% confidence intervals, in months.
- Cox proportional-hazards (Cox-PH) models will be used to test the association of PSA progression with overall survival, progression-free survival and clinical progression-free survival. Other covariates that show a significant (p<0.05) association with survival in the univariable Cox-PH model will be included in the multivariable Cox-PH model. If a skewed distribution is observed in any of the continuous variables, logarithmic transformation may be performed. Tests of proportionality based on Schoenefeld residuals will be applied to test the proportional hazards assumption.
- The performance of the multivariable cox-PH survival models will be evaluated by calculating Uno’s inverse-probability weighted c-index and time-dependent incident dynamic ROC AUC curve values (established around the median survival of the dataset).

The COU-AA-301 dataset will be used as a test set, and the COU-AA-302 dataset will be used as a validation dataset. All analyses will be performed in the intent-to-treat populations initially, and separately in each of the trial study arms.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/304">NCT00638690 - COU-AA-301 - A Phase 3, Randomized, Double-Blind, Placebo-Controlled Study of Abiraterone Acetate (CB7630) Plus Prednisone in Patients With Metastatic Castration-Resistant Prostate Cancer Who Have Failed Docetaxel-Based Chemotherapy</a></li><li><a href="/node/1115">NCT00887198 - COU-AA-302 - A Phase 3, Randomized, Double-blind, Placebo-Controlled Study of Abiraterone Acetate (CB7630) Plus Prednisone in Asymptomatic or Mildly Symptomatic Patients With Metastatic Castration-Resistant Prostate Cancer</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2018

2017-2511

Project Title: 
Policy-aware evaluation of personalized treatment strategies
Specific Aims of the Project: 

The aims of this project are:
(1) To develop statistical tests for the benefit of personalization.
(2) To develop innovating approaches to determine individualized treatment strategies using data from randomized controlled trials.
(3) To illustrate the potential gain of the approach we propose using real data from randomized controlled trials.
The request for data to the Yoda platform primarily serves this third aim. As a corollary, this will allow ultimately to determine individualized treatment strategies for diabetic patients with an associated measure of potential population benefit.

What type of data are you looking for?: 
Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Application Status

Ongoing
Scientific Abstract: 

Background: Personalized or precision medicine aims at giving the “right treatment to the right patient”. It is one of the most promising areas of medical research, but its development is hindered by methodological limitations of studies. Determining individualized treatment rules (ITR) is an active research field of biostatistics relying on recent statistical methods (machine learning, classification of high-dimensional data) that raises many statistical and computational challenges. For instance measures of the benefit of an ITR as compared to a ‘one size fits all’ treatment strategy where all patients receive the treatment performing best on average have been proposed. Properly testing whether personalization provides overall benefit after estimating an ITR however remains an open problem.
Objective: We aim at developing a statistical test for the benefit of personalization, as well as innovative approaches to identify ITRs, and to apply these methods to real data.
Study design: Retrospective analysis of randomized controlled trial.
Participants: Diabetes patients included in the trial and receiving at least one dose of study drug.
Main Outcome measure: Change in HbA1c from baseline to week 52.
Statistical analysis: The estimation of ITR will rely on modeling the outcome using treatment arm and covariates using random forests. The estimating and test of the benefit of the ITR will account for the uncertainty in the ITR estimation, and provide proper confidence intervals as well as control of the type I error rate.

Brief Project Background and Statement of Project Significance: 

The objective of personalized or precision medicine is to give “the right patient the right drug at the right moment”. Precision medicine therefore implies determining which treatment is the best for a given patient, based on his/her characteristics, instead of favoring the one with better outcome on average in the whole population (one size fits all). Indeed, the current practice in medicine is to favor the treatment with the highest response rate (response being intended in the general sense of any favorable outcome). This would be a reasonable rule only if the responders to the “inferior” treatment would all respond to the “superior” one. However, when sets of responders do not overlap, an individualized treatment strategy could lead to a much higher response rate in the overall population. For instance if the usual treatment strategy only has a 20% response rate and 40% of patients respond to the new treatment, the response rate in the overall population could range from 40% (if all responders to the usual treatment respond to the new one) to 60% (if none of them respond to the new treatment).
Developing methods to identify patients more likely to respond to a treatment than to another one has recently become a very active research topic in biostatistics. Once a model predicting whether a patient would be more likely to respond to a given treatment or to its comparator has been obtained, for example using data from a randomized controlled trial (RCT), it is straightforward to derive an individualized treatment rule (ITR) where patients would receive the treatment under which their predicted response is higher. It has been shown that such a strategy would maximize the expectation of the outcome over the population.
Measures of the performance of an ITR using a biomarker as compared to the “one size fits all” strategy have also been developed, such as the improvement in population average outcome under the ITR, for instance. The specification of the model to estimate the performance of the ITR is however important, especially when several biomarkers are considered together. The classical approach relies on generalized linear regression with markers by treatment interactions, but the effect of model misspecification can be critical, especially around the decision boundary. This would lead to biased predicted performance of ITR. Even using flexible approaches such as machine learning, there remains a non-negligible risk to recommend the incorrect treatment for patients with close predicted response under each treatment. In addition, testing whether personalization provides overall benefit remains an open problem.

Statement of project significance
Our work is important for two main reasons. First, it is crucial that responders to each treatment compared would be correctly identified to develop individualized treatment strategies that will improve the outcome of patients. In that respect, there is a need for cutting-edge statistical methods. Second, it is also important that the benefit of such individualized strategies would not be overstated and overestimated because there is a risk of false decision at potentially high costs.

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

Selection of the trials:
The methods we develop need RCTs with relatively large sample size, as well as relevant patients characteristics. Based on these considerations, we have selected the study NCT00968812 (CANTATA-SU trial) as a possible good candidate for our methodology, since it has a large sample size, the experimental and control treatments have different mechanisms of action, which is likely better suited for finding variables associated with a differential treatment effect, and the effect of the experimental treatment as compared to the comparator is not overwhelming, thus allowing for a more refined strategy.

Selection of the patients:
All patients included in the selected trial and receiving at least one dose of study drug (modified intent to treat analysis as reported in the study primary reports) will be considered.

Narrative Summary: 

In therapeutic evaluation, the treatment with the highest response rate (or average outcome) is usually considered as superior to the others. This would however be true only if the responders to the “inferior” treatment would all respond to the “superior” one. When sets of responders do not overlap, an optimal treatment strategy could lead to a much higher response rate in the overall population
In this project, we aim at developing statistical methods to both identify individualized treatment rules targeting the responders to each treatment, and evaluate the population benefit of using such rules.

Project Timeline: 

We are currently working on the methodological developments and performing simulation studies to investigate the properties of our procedure in realistic settings. Analyzing the trial should be straightforward once they are in an analysis-ready format. Depending on the format of data provided, however, this could imply additional data management tasks. We however plan to have the analyses ready in 6 to 8 months.

Dissemination Plan: 

Our primary purpose is to illustrate how the methods we develop perform in real settings. To this aim, we plan to draft a first article for a statistical journal such as JASA or Biometrics, where the data would serve as illustration only.
Then we plan to draft also a clinical article for a medical audience (in a journal such as BMJ, PloS Medicine, BMC Medicine, or a specialty journal such as Diabetes or Diabetes Care), where the results of the study would be presented for non-statisticians, expecting a clinical impact of our project.

Bibliography: 

Cai T, Tian L, Wong PH, Wei LJ. Analysis of randomized comparative clinical trial data for personalized treatment selections. Biostatistics 2011; 12:270–282.
Foster JC, Taylor JM, Ruberg SJ. Subgroup identification from randomized clinical trial data. Stat Med 2011; 30:2867–2880.
Huang EJ, Fang EX, Hanley DF, Rosenblum M. Inequality in treatment benefits: can we determine if a new treatment benefits the many or the few? Biostatistics 2017; 18(2):308–324.
Huang Y, Fong Y. Identifying optimal biomarker combinations for treatment selection via a robust kernel method. Biometrics 2014; 70(4):891–901.
Huang Y, Laber EB, Janes H. Characterizing expected benefits of biomarkers in treatment selection. Biostatistics. 2015;16(2):383–99.
Janes H, Brown MD, Huang Y, Pepe MS. An approach to evaluating and comparing biomarkers for patient treatment selection. Int J Biostat 2014; 10(1):99–121.
Janes H, Pepe MS, McShane LM et al. The fundamental difficulty with evaluating the accuracy of biomarkers for guiding treatment. J Natl Cancer Inst 2015; 107(8):djv157.
Kang C, Janes H, Huang Y. Combining biomarkers to optimize patient treatment recommendations. Biometrics 2014; 70(3):695–707.
Li J, Zhao L, Tian L, Cai T, Claggett B, Callegaro A, Dizier B, Spiessens B, Ulloa-Montoya F, Wei LJ. A predictive enrichment procedure to identify potential responders to a new therapy for randomized, comparative controlled clinical studies. Biometrics. 2016; 72(3):877–887.
Lipkovich I, Dmitrienko A, Denne J, Enas G. Subgroup identification based on differential effect search - a recursive partitioning method for establishing response to treatment in patient subpopulations. Stat Med 2011; 30:2601–2621.
Porcher R, Jacot J, Biau D. Identifying treatment responders using counterfactual modeling and potential outcomes. Presented at EpiClin 2016, manuscript submitted.
Qian M, Murphy SA. Performance guarantees for individualized treatment rules. Ann Stat 2011; 39(2):1180–1210.
Shalit U, Johansson F, Sontag D. Estimating individual treatment effect: generalization bounds and algorithms. arXiv:1606.03976; 2016.
Shen J, Wang L, Taylor JMG. Estimation of the optimal regime in treatment of prostate cancer recurrence from observational data using flexible weighting models. Biometrics 2017;73(2):635–645.
Shen J, Wang L, Daignault S, Spratt DE, Morgan TM, Taylor JMG. Estimating the optimal personalized treatment strategy based on selected variables to prolong survival via random survival forest with weighted bootstrap. J Biopharm Stat 2017 (Ahead of print).
Su X, Tsai CL, Wang H, Nickerson DM, Li B. Subgroup analysis via recursive partitioning. J Mach Learn Res 2009; 10:141–158.
Zhang B, Tsiatis AA, Laber EB, Davidian M. A robust method for estimating optimal treatment regimes. Biometrics 2012; 68:1010–1018.
Zhao L, Tian L, Cai T, Claggett B, Wei LJ. Effectively selecting a target population for a future comparative study. J Am Stat Assoc 2013;108:527–539.
Zhao YQ, Zeng D, Laber EB et al. Doubly robust learning for estimating individualized treatment with censored data. Biometrika 2015; 102(1):151–168.
Wager S, Athey S. Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc. 2017 (ahead of print).

What is the purpose of the analysis being proposed? Please select all that apply.: 
News research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations
Preliminary research to be used as part of a grant proposal
Other
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

Our main outcome will be the same as the primary study outcomes, i.e. change in HbA1c from baseline to week 52.

To illustrate the potential of the method for a binary outcome, which has been more frequent in statistical articles on the issue of individualized treatment strategies, we will add a binary key secondary outcome, which will be the proportion of patients achieving HbA1C <7.0% (53 mmol/mol).

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

The primary independent variable is the treatment arm allocated. Since the CANTATA-SU comprises three treatment arms, we will use as primary comparison the comparison of canagliflozin 300 mg + metformin versus glimepiride + metformin. In a second stage, we will perform a similar analysis for canagliflozin 100 mg + metformin versus glimepiride + metformin.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

The other variables of interest are the patient characteristics that will be used to construct a model for individual treatment. In order to capture at best the heterogeneity in treatment effect, as many baseline (pre-randomization) variables as possible should be considered in our models. We list here a minimal set of variables that could be used:
Sex
Age
Race
Glycated hemoglobin A1c (HbA1c)
Fasting plasma glucose (FPG)
Bodyweight
Body-mass index
Duration of type 2 diabetes
Whether patient entered antihyperglycemic drug adjustment period
Smoking history
Other diseases or comorbidities (whenever available)
Systolic blood pressure
Diastolic blood pressure
Pulse rate
Triglycerides
LDL cholesterol
HDL cholesterol
Non-HDL cholesterol
Insulin
Alanine aminotransferase
Aspartate aminotransferase
Alkaline phosphatase
Bilirubin
Blood urea nitrogen
Gamma-glutamyltransferase
Urate
Hemoglobin
Urine albumin/creatinine
Total fat mass
Total lean mass
Subcutaneous adipose tissue
Visceral adipose tissue

Statistical Analysis Plan: 

The analysis will consider a counterfactual outcomes framework, where we posit that for each patient, there exists two potential outcomes, Y(1) and Y(0), representing the outcome that the patient would experience should s/he receive the studied treatment (indexed by 1) or its comparator (indexed by 0), respectively. This allows defining a counterfactual individual treatment effect as D = Y(1) – Y(0). In practice, D cannot be observed, except under very specific trial designs such as n-of-1 trials. The question of precision medicine is thus rather to estimate the expected value of D given a set of covariates X. Assuming that higher values of Y represent a more favorable outcome, it has been shown that the optimal individualized treatment rule given X—or optimal treatment regime—corresponds to give treatment 1 to patients with D(X)=E(D|X) > 0 and treatment 0 to patients with D(X) < 0. For those with D(X)=0, the decision to favor one of the treatment should be based on other considerations, such as favoring the treatment with higher outcome on average.
Let us assume that the new treatment (here canaglifozin) performs better than its comparator. Deriving an individualized treatment rule (ITR) therefore relies on identifying the set of patients for whom the predicted treatment effect is negative, after modeling the outcome under each treatment. In our methodological work, we however show the poor statistical properties of such a policy in practice, with a high risk of identifying patients as “non-responders” when in fact they derive benefit from the treatment. We therefore intend to use an approach that we have developed and termed ‘policy-aware’ to the analysis of data, that we have shown to outperform the classical approach.
Let us introduce some notations, to facilitate the description of our approach. An ITR or policy p, maps the vector of patients characteristics X to {0,1}, representing the treatment, so that p(X) is either 1 (canaglifozin should be given) or 0 (glimepiride should be given). The population benefit of using the policy p as compared to giving canaglifozin to all is K(p) = E(Y|p is used) – E(Y|all receive canaglifozin) = E[-D(X) | p(X)=0], where D(X) is E(D|X). In practice, however, D(X) is unknown, and is estimated from the data as d(X)=Ê(Y(1) – Y(0)|X).

The analysis will consist of the following steps:
1. Develop a model for the outcome using the treatment arm and covariates using random forests, in order to allow more flexibility in the model.
2. Derive features from the random forests that are subsequently used as a single covariate in a (generalized) linear model for the outcome with effects for the feature, the treatment and their interaction.
3. Randomly sample 1000 times in the posterior distribution of the model parameters to derive a vector of subject-specific treatment effects d(X) predicted by injecting the sampled parameters into the regression model, and convert this vector into a z-score by taking the mean divided by the standard deviation, resulting in a z(X).
4. Determine the threshold r which maximizes the expected population benefit of personalization K(p) when only those for with z(X) > r receive canaglifozin, by searching a grid from -2.05 (0.02-quantile of a standard normal distribution) to 0.
5. Provide estimates of the expected population benefit of this policy p obtained with the optimized value of r, that we term ‘max lower bound policy’, with associated confidence interval.

When analyzing changes in HbA1c, a linear model will be used, but for analysis of the proportion of patients achieving HbA1c < 7%, then we will rely on a logistic model.

In this project, missing outcome and predictor values will be simply ignored for the analyses that only aim at illustrating the potential of the method to a statistical audience. On the contrary, for the real clinical application, they will be handled through multiple imputation by chained equations.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/312">NCT00968812 - 28431754DIA3009 - A Randomized, Double-Blind, 3-Arm Parallel-Group, 2-Year (104-Week), Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of JNJ-28431754 Compared With Glimepiride in the Treatment of Subjects With Type 2 Diabetes Mellitus Not Optimally Controlled on Metformin Monotherapy</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2019

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