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2020-4165

Project Title: 
Exploring Novel Methods to Estimate Heterogeneous Treatment Effects
Specific Aims of the Project: 

The overall aim of this study is to promote the use of appropriate statistical methods to inform personalization of medicine. My objective is to explore the relative performance of alternative methods for the estimation of heterogeneous treatment effects in contexts that commonly occur in the real world and to demonstrate their performance in clinical case studies. The research questions (RQ) are as follows:
RQ1: Which Machine learning methods are appropriate for identifying heterogeneous treatment effects in Randomized Controlled Trials?
RQ2: How sensitive are the various Machine Learning approaches to features commonly encountered in clinical settings including (a) small samples, (b) clustering in the trial design, (c) large numbers of potential treatment moderators and (d) alternative outcome types (binary, count, survival time)?
RQ3: In the case studies I consider, how strong is the evidence for heterogeneous treatment effects, what drives this heterogeneity and is it of clinical relevance?

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

Application Status

Approved Pending DUA Signature
Scientific Abstract: 

Background
In the literature, a variety of methods have been used to estimate HTE however their relative performance in clinical contexts remains largely unknown. This project will use a combination of Monte Carlo simulations and a real-world application to address this knowledge gap with a view to promoting the uptake of suitable methods for Personalized medicine.
Objective:
To assess the performance of methods to estimate HTE and to evaluate the effect of canagliflozin compared with glimepiride on outcomes including:
• Blycemic control (HbA1C and fasting plasma glucose [FPG])
•Body weight, waist circumference, and BMI.
•Incidence of hypoglycemia
•Systolic blood pressure (SBP) and diastolic one (DBP)
•Time to receiving rescue therapy or discontinuing due to need for rescue therapy
•Proportion of subjects receiving rescue therapy or discontinuing due to need for rescue therapy through Week 104
•Urinary glucose excretion (UGE)
Study Design;
This study is a randomized, double-blind, 3-arm, parallel-group, active-controlled, multicenter study. We will also simulate data based on the RCT data.
Participants; A total of 1,452 subjects that are randomized to glimepiride, canagliflozin 100 mg, and canagliflozin 300 mg in a 1:1:1 manner.
Main Outcome Measure(s); Change in HbA1c From Baseline to Week 52
Analysis
We'll primarily use causal forest modelling and BART to identify Heterogeneous Treatment Effects on the actual RCT data. We will conduct a Monte Carlo simulation study to test the performance of the methods in a context where the true HTE is known.

Brief Project Background and Statement of Project Significance: 

A common approach to HTE analysis is to compare binary groups (such as male vs female), or to interact a treatment identifier with a range of covariates. However, such comparisons make strong assumptions regarding the role of other covariates and the form of effect modification. Few such subgroup effects are corroborated in subsequent studies (Wallach et al 2016; Wallach et al 2017). Kent et al (2018) suggest that many or even most statistically significant subgroup effects represent false discoveries. Kent et al (2018 BMJ) highlight that flexible machine learning (ML) methods may be helpful in this context. I have conducted a preliminary scoping exercise to identify methods that may be useful in this context including regression trees (Su et al, 2009; Athey & Imbens, 2016), Random Forests (Wager & Athey,2018; Athey, Tibshirani, & Wager, 2019), Causal Forests (Athey et al 2018) , the least absolute shrinkage and selection operator (Lasso) (Qian & Murphy, 2011;Tian et al, 2014; Chen et al, 2017), Support Vector Machines (Imai & Ratkovic, 2013), Boosting (Powers et al.,2018), Neural Networks (Johansson et al, 2016; Shalit et al 2016; Schwab et al 2018) and Bayesian Additive Regression Trees (BART) (Hill, 2011; Taddy et al, 2016). It is imperative that strong evidence-based foundations are developed to support clinicians in treatment decision making. This research will advance knowledge through several avenues by: (1) identifying statistical approaches, particularly those using Machine Learning, that can reliably estimate HTEs; (2) exploring their performance in simulation studies designed to reflect real world applications; (3) applying the best-performing methods to our case studies to identify patients that are most likely to benefit from targeted interventions.

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

Inclusion and exclusion criteria are as used in the RCT and are listed below
Inclusion Criteria:
Patients must have a diagnosis of type 2 diabetes
Body mass index (BMI) should be between 22 and 45 kg/m2 at screening
Patients must be taking a stable dosage of metformin as monotherapy at screening
Patients must have a HbA1c between >=7% and <=9.5% at Week 2
Patients must have a fasting plasma glucose (FPG) <=270 mg/dL (15 mmol/L) at Week -2

Exclusion Criteria:
Patients having prior exposure or known contraindication or suspected hypersensitivity to JNJ-28431754, glimepiride, or metformin
History of diabetic ketoacidosis or type 1 diabetes mellitus
History of pancreas or beta-cell transplantation
History of active proliferative diabetic retinopathy
History of hereditary glucose-galactose malabsorption or primary renal glucosuria
Renal disease requiring treatment with immunosuppressive therapy within the past 12 months before screening or a history of dialysis or renal transplant
Taken thiazolidinedione therapy in the past 16 weeks before screening

Narrative Summary: 

Personalised medicine to improve population health, requires evidence on how the relative effectiveness and harms of alternative treatments, or treatment regimes (frequency, dosage or combinations of drugs) differ across individual patients. The effect of treatment for particular patients is likely to differ according to their baseline characteristics (such as age, gender, severity of disease) in addition to the treatment regime itself.
I will use flexible data-driven approaches, mainly coming from the Machine Learning literature, to improve the identification and estimation of heterogeneous treatment effects (HTE).

Project Timeline: 

I have started the preparation for my project on 1/11/2020 and It is expected to be completed by September 2021. Therefore, the project timeline would be as follows:
Months 1-3 - Data preparation and design of simulations
Months 4-11 - Analysis and conducting simulations
Months 12-15 - write up of initial analysis.
Months 16-18 - dissemination of results.
The manuscript will be drafted and submitted for publication by 1/1/2021. All manuscripts, abstracts, posters and presentations will be shared with the YODA Project at the time of submission.

Dissemination Plan: 

My research will lead to at least 2 internationally peer reviewed publications, which will be targeted to leading journals in medical statistics, causal inference and health economics, including Statistics in Medicine, Journal of Causal Inference, Journal of Health Economics, Medical Care and Medical Decision Making, and clinical journals where relevant such as Diabetes, and Diabetes Care. In line with the IRC’s Open Access policy and the National Principles for Open Access Policy Statement (2012), NUIG requires that authors of peer-reviewed articles and peer-reviewed conference papers must deposit a copy in the University’s open access repository ‘ARAN’. This will enhance the use and impact of my PhD research I plan to disseminate my research by presenting at leading national and international conferences such as the Irish Economic Association (IEA) conference, ISPOR Annual International Meeting, American Society of Health Economists,
European Health Economics Association Conference, Society for Medical Decision Making (SMDM) conference, International Health Economics Association (IHEA) conference, European Causal Inference Meeting and the International Conference on Health Policy Statistics conference.

Bibliography: 

-Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized random forests. The Annals of Statistics, 47(2), pp.1148-1178.
-Athey, S., & Imbens, G. W. (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27), 7353– 7360.
-Chen, S., Tian, L., Cai, T., & Yu, M. (2017). A general statistical framework for subgroup identification and comparative treatment scoring. Biometrics, 73(4), 1199–1209.
-Hill, J. L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20(1), 217–240.
-Imai, K., & Ratkovic, M. (2013). Estimating treatment effect heterogeneity in randomized program evaluation. Annals of Applied Statistics, 7(1), 443–470.
-Johansson, F., Shalit, U., & Sontag, D. (2016). Learning representations for counterfactual inference. In International Conference on Machine Learning (pp. 3020–3029).
-Knaus, M.C., Lechner, M. and Strittmatter, A., 2018. Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence. Retrieved from http://arxiv.org/abs/1810.13237.
-Nie, X., & Wager, S. (2017). Quasi-oracle estimation of heterogeneous treatment effects. Retrieved from http://arxiv.org/abs/1712.04912.
-Powers, S., Qian, J., Jung, K., Schuler, A., Shah, N. H., Hastie, T., & Tibshirani, R. (2018). Some methods for heterogeneous treatment effect estimation in high
dimensions. Statistics in Medicine, 37(11), 1767–1787.
-Qian, M., & Murphy, S. A. (2011). Performance guarantees for individualized treatment rules. Annals of Statistics, 39(2), 1180.
-Schwab, P., Linhardt, L., & Karlen, W. (2018). Perfect match: A simple method for learning representations for counterfactual inference with neural networks Retrieved from http://arxiv.org/abs/1810.00656.
-Shalit, U., Johansson, F. D., & Sontag, D. (2016). Estimating individual treatment effect: Generalization bounds and algorithms. Retrieved from ttp://arxiv.org/abs/1606.03976.
-Su, X., Tsai, C.-L., Wang, H., Nickerson, D. M., & Li, B. (2009). Subgroup analysis via recursive partitioning. Journal of Machine Learning Research, 10(Feb), 141–158.
-Taddy, M., Gardner, M., Chen, L., & Draper, D. (2016). A nonparametric Bayesian analysis of heterogeneous treatment effects in digital experimentation. Journal of Business & Economic Statistics, 34(4), 661–672.
-Tian, L., Alizadeh, A. A., Gentles, A. J., & Tibshirani, R. (2014). A simple method for estimating interactions between a treatment and a large number of covariates. Journal of the American Statistical Association, 109(508), 1517–1532.
-Wallach JD, Sullivan PG, Trepanowski JF, Steyerberg EW, Ioannidis JP(2016). Sex based subgroup differences in randomized controlled trials: empirical evidence from Cochrane meta-analyses. BMJ2016;355:i5826. doi:10.1136/bmj.i5826 pmid:27884869.
-Wallach JD, Sullivan PG, Trepanowski JF, Sainani KL, Steyerberg EW, Ioannidis JP (2017). Evaluation of Evidence of statistical support and corroboration of subgroup claims in randomized clinical trials. JAMA Intern Med2017;177:554-60. doi:10.1001/jamainternmed.2016.9125 pmid:28192563.
-Kent, D.M., Steyerberg, E. and van Klaveren, D., (2018). Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. Bmj, 363, p.k4245.

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 that confirms or validates previously conducted research on treatment safety
Develop or refine statistical methods
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

The primary endpoint will be the change in HbA1c from baseline to week 52, with a non-inferiority margin of 0·3% for the comparison of each canagliflozin dose with glimepiride. If non-inferiority is shown, we will assess superiority on the basis of an upper bound of the 95% CI for the difference of each canagliflozin dose versus glimepiride of less than 0·0%. Analysis will be done in a modified intention-to-treat population, including all randomised patients who received at least one dose of study drug.

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

We will choose the doses of canagliflozin on the basis of previously published findings from a dose-ranging, canagliflozin 100 mg canagliflozin 300 mg as well as glimepiride treatment ranged from a starting dose of 1 mg to a maximum dose of 6 mg or 8 mg (on the basis of maximum approved dose in the country of the investigational site).

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

The specified secondary efficacy endpoints are percentage change from baseline in body-weight, and proportion of patients with documented hypoglycemic episodes, including biochemically documented episodes (concurrent finger-stick glucose or plasma glucose less than or equal 3.9 mmol/L with or without symptoms) and severe episodes (those needing assistance of another individual or resulting in seizure or loss of consciousness). Additional endpoints included the proportion of patients achieving HbA1c less than either 7·0% or 6·5%; change in fasting plasma glucose and systolic and diastolic blood pressure; and percentage change in fasting plasma lipids, including HDL cholesterol, triglycerides, LDL cholesterol, non-HDL cholesterol, and ratio of LDL cholesterol to HDL cholesterol.

Statistical Analysis Plan: 

To address RQ1 and RQ2 we will design a Monte Carlo simulation study, based on the observed correlations in the actual trial data to assess the relative performance of the statistical methods (Causal forests, BART and other methods identified). For each simulated patient, we will simulate potential outcomes under control (Y0) based on the control arm of the trial data, and potential outcomes under treatment (Y1) based on the treatment arm of the trial data. Hence the true HTE for each individual can be defined as (Y1-Y0) and is known by construction, allowing measures of estimation bias and precision to be calculated for each estimation method. We will consider a range of possible data generating model specifications for the potential outcomes (and hence for the HTE), ranging from no heterogeneity, heterogeneity due to a single covariate and complex patterns of effect modification. We will simulate scenarios (a) under a range of sample sizes (100, 500, 1000 & 5000 observations), (b) different trial designs (clustered/not clustered), (c) various numbers of potential treatment moderators/covariates and (d) alternative outcome types (binary, continuous, count, survival time).
Methods will be compared in terms of their percentage bias and RMSE for individual effects and aggregated subgroup effects as well as for the overall average treatment effect.
To address RQ3, we will apply each of the chosen algorithms (Causal Forest, BART and any other promising methods identified during the study) to the actual data-set to estimate heterogeneous treatment effects. The outcome will be Change in HbA1c From Baseline to Week 52, exclusion and exclusion criteria will be as described above. The covariates that will be included in the models include the following baseline variables: gender, age, race, HbA1c (%), FPG (mmol/L), Body-weight (kg), Body-mass index (kg/m2), Duration of type 2 diabetes (years), and Whether they entered an antihyperglycaemic drug adjustment period. Since the original study is likely to be underpowered to detect subgroup effects at this level, we consider the results to be hypothesis generating, rather than a means to conclusively identify subgroups that clinically benefit. A change of 0.5% (5.5 mmol/mol) will be considered clinically meaningful for this study.

How did you learn about the YODA Project?: 
Software Used: 
RStudio
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 : 

2019-4092

Project Title: 
Cross-Trial Comparisons of Biologic Therapies for Auto-Immune Diseases
Specific Aims of the Project: 

1) We have proposed a general statistical method that allows for robust estimation of the proportion of treatment effect that can be explained by surrogate markers. To validate our method, the primary aim of this study will be to examine the proportion of treatment effect explained by clinical remission at week 8 on the outcome of interest – clinical remission at week 52 in patients with active UC. This will be assessed in cross-trial comparisons for infliximab vs. placebo and golimumab vs. placebo using network meta-analysis methods. Clinical efficacy will be assessed by rates of clinical remission as measured by a Mayo Score <2 for UC. Secondary endpoints will include clinical response, endoscopic healing, adverse events, rates of antibody formation, and health-related quality of life.
2) The secondary aim of this study will be to examine the proportion of treatment effect explained by the surrogate marker – antiocardiolipin antibodies (aCL) at week 8 on the outcome of interest – aCL incidence at week 54 in RA patients.

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

Approved Pending DUA Signature
Scientific Abstract: 

Background: Biologics are used widely in patients with UC and RA but head-to-head comparisons of such therapies are lacking.
Objective: To conduct cross-trial comparisons of biologics for UC and RA using a network meta-analysis approach and to validate a novel statistical method that examines the proportion of treatment effect explained by surrogate markers.
Study Design: In comparisons of UC biologics, participants will be grouped into those in trials for infliximab vs. placebo and in trials for golimumab vs. placebo. The primary outcome will be clinical remission at week 52. The surrogate measure is clinical remission at week 8. In comparisons of RA biologics, participants will be grouped into those in trials for golimumab vs. golimumab in combination with methotrexate and trials for infliximab in combination with methotrexate vs. methotrexate. The primary outcome will be change from baseline of anticardiolipin antibodies (aCL) at week 54.
Participants: Adults ≥ 18 years who meet the study inclusion criteria will be included.
Main Outcome Measures: The main outcome measures include clinical response, clinical remission, and incidence of the development of aCL. Additional outcomes of interest include endoscopic healing, antibody formation, and health-related quality of life.
Statistical Analysis: Inverse probability weighted and doubly robust estimators will be obtained for the proportion of treatment effect explained by surrogates. Network meta-analysis will be performed and consistency assumptions will be checked using net heat plots and node splitting.1,2

Brief Project Background and Statement of Project Significance: 

Randomized controlled trials aim to identify efficacious and safe treatments to improve health and reduce the risk of negative outcomes. However, RCTs are typically long and costly, and there has been a growing interest in identifying and validating surrogate markers to infer treatment effects on outcomes.3,4,5 The identification and validation of appropriate surrogate markers has the potential to allow for earlier testing of treatment effects, thus reducing the cost of expensive trials, cutting the time to market for new therapies, and decreasing patient burden if the true endpoint is invasive.

We have proposed inverse-probability weighted and doubly robust estimators for the proportion of treatment effect explained by surrogate markers. To validate our statistical method, we aim to examine a data application of real-world interest – head-to-head trials of biologic therapies for patients with UC and RA. UC is a chronic inflammatory disorder of the large bowel and is characterized by bloody diarrhea, fecal urgency, and abdominal pain.6 When conventional treatments such as corticosteroids fail, biologic therapies such as infliximab or golimumab are often used.7 While these therapies have been shown to be effective in placebo-controlled RCTs, and there exist some head-to-head trials comparing biologic therapies for RA patients, there have been very few trials directly comparing biologic therapies in UC patients.8 One of the first such trials, a phase 3b trial of vedolizumab vs. adalimumab for patients with moderate-to-severe UC, was conducted at 245 centers in 34 countries, and showed that vedolizumab was superior to adalimumab with respect to achievement of clinical remission and endoscopic improvement, but not corticosteroid-free clinical remission.9 However, the researchers were unable to postulate an explanation for the inconsistency of the results between the primary and secondary remission outcomes, and conclude that this question requires further investigation. We propose to conduct a network meta-analysis to make head-to-head comparisons between biologic therapies for UC.

In summary, there is a need for more cross-trial data evaluating the efficacy of biologic treatments in UC patients, and analysis of such data with our novel statistical method and network meta-analysis methods may allow for the identification and validation of surrogate markers for important UC clinical outcomes.

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

The database will be queried for all clinical trial data evaluating efficacy and safety of biologic therapies in UC or RA patients. Based on a preliminary evaluation of the data available on the website, we expect to procure data related to infliximab and golimumab use. Additional drugs of interest, should they become available in the interim, would include adalimumab, vedolizumab, and/or methotrexate. All patients included in the original clinical trial data will be included in this sub-analysis.

Narrative Summary: 

The incorporation of biologic therapies into clinical practice has improved the medical management of rheumatoid arthritis (RA) and inflammatory bowel disease (IBD), which includes Crohn’s disease (CD) and ulcerative colitis (UC). When conventional treatments fail, biologic agents such as infliximab, adalimumab, or golimumab may be used. While these medications have been shown to be effective in placebo-controlled trials, there has been a lack of trials comparing agents directly. This study proposes validating a novel statistical method for the comparison of biologic therapies across trials by performing network meta-analysis utilizing a repository of existing biologic trial data.

Project Timeline: 

Anticipated Project Start Date (data access granted): March 1, 2019
Analysis Completion Date: May 1, 2019
Report of Results to YODA: June 1, 2019
Date of First Manuscript Draft: June 15, 2019
Date of Manuscript Submission: July15, 2019

Dissemination Plan: 

The expected audience for this work includes biostatisticians who are interested in the development and application of our novel statistical method, as well as practicing general gastroenterologists, IBD sub-specialists, and rheumatoid arthritis specialists. Potential journals for submission include Biometrika, Biometrics, and Statistics in Medicine.

Bibliography: 

1. Krahn, U., Harald, B. and Jochem K. (2013). A graphical tool for locating inconsistency in network meta-analyses. BMC Med Res Methodology; 13(1). Biomed Central: 35.
2. Dias, S., Welton, N., Caldwell, D., and Ades A. (2010). Checking consistency in mixed treatment comparison meta-analysis. Statistics in Medicine; 29(7-8): 932-944.
3. Prentice, R.L. (1989). Surrogate endpoints in clinical trials: definition and operational criteria. Statistics in Medicine; 8, 431-440.
4. Price, B., Gilbert, P. and van der Laan, M. (2018). Estimation of the optimal surrogate based on a randomized trial. Biometrics; 74(4): 1271-1281.
5. Colombel, J., Rutgeerts, P., Reinisch, W., et al. (2011). Early mucosal healing with infliximab is associated with improved long-term clinical outcomes in ulcerative colitis. Gastroenterology; 141(4): 1194-1201.
6. Ungaro, R., Mehandru, S., Allen, P. Peyrin-Biroulet L., Colombel, J. (2017). Ulcerative colitis. Lancet; 389: 1756-1770.
7. Rubin, D. Ananthakrishnan, A., Siegel, C., Sauer, B., Long, M. (2019). ACG clinical guideline: ulcerative colitis in adults. Am J Gastroenterol.; 114: 384-413.
8. Taylor, P. Keystone, E., van der Heijde, D., et al. (2017). Baricitinib versus placebo or adalimumab in rheumatoid arthritis. NEJM; 376: 652-662.
9. Sands, B., Peyrin-Biroulet, L., Loftus, E., et al. (2019). Vedolizumab versus adalimumab for moderate-to-severe ulcerative colitis. NEJM; 381: 1215-1226.

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
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
Develop or refine statistical methods
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: 

The primary outcome evaluated will be clinical remission at week 52. Clinical remission will be defined as a Mayo Score less than 2 and no subscore > 1 on any of the four subcomponents for UC.

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

The main predictor variable will be the binary surrogate marker, i.e. clinical remission at week 8.

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

Additional variables of interest will include clinical response, endoscopic healing, adverse events, antibody formation, and health-related quality of life. Confounders of interest, such as patient demographic data (age, sex, ethnicity) will be used to control for baseline differences. Clinical response will be defined as a decrease in the Mayo score by 2 (with all subscores less than or equal to 1) in UC. Endoscopic activity will be defined as mucosal healing: a Mayo endoscopic subscore of 0 or 1 for UC. Antibody formation will be assessed by trough drug level and detectable antibodies. Finally, health-related quality of life will be assessed with the SIBD-Q score.

Statistical Analysis Plan: 

Comparison of continuous variables will be performed by t tests or Mann-Whitney test, as appropriate. Comparison of categorical variables will be performed by Chi square tests or Fisher Exact tests, as appropriate. Our proposed inverse-probability weighted and doubly robust estimators will be used to estimate the proportion of treatment effect explained by surrogate markers on the outcome. Network meta-analysis methods will be used to make cross-trial comparisons of infliximab vs. golimumab despite no clinical trials directly comparing the two drugs for UC patient outcomes.

How did you learn about the YODA Project?: 
Software Used: 
RStudio
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/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/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/356">NCT00269867 - C0168T22 - A Placebo-Controlled, Double-Blinded, Randomized Clinical Trial of Anti-TNF Chimeric Monoclonal Antibody (cA2) in Patients With Active Rheumatoid Arthritis Despite Methotrexate Treatment</a></li><li><a href="/node/357">NCT00236028 - C0168T29 - A Randomized, Double-blind, Trial of Anti-TNFa Chimeric Monoclonal Antibody (Infliximab) in Combination With Methotrexate Compared With Methotrexate Alone for the Treatment of Patients With Early Rheumatoid Arthritis</a></li><li><a href="/node/755">NCT01551290 - CR018769; REMICADEUCO3001 - 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/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/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></ol>
Make Publicly Available : 

2019-4078

Project Title: 
Consistency checks to improve measurement with the Personal and Social Performance (PSP) scale
Specific Aims of the Project: 

Apply algorithm developed based on expert opinion to detect scoring inconsistencies in the use of the Personal and Social Performance scale. Algorithm to be applied to data sets from various clinical trials. Recommendations based on this work to be published.

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: Symptom manifestations in schizophrenia can be subtle. Small imprecisions in measurement can lead to over- or under-estimation of change. One strategy to improve measurement is to conduct logical consistency checks between item responses (i.e., cross-sectionally) and across test administrations (i.e., longitudinally) of rating scales, bearing in mind that some degree of inconsistency is to be expected due to subject-based variability. Objective: To determine the relevance of continuity checks to data from the Personal and Social Performance Scale (PSP). Study Design: International Society for CNS Clinical Trials and Methodology convened an expert Working Group that assembled consistency/inconsistency flags for the Personal and Social Performance Scale (PSP). Participants: Data sets are being requested from sponsors who conducted clinical trials that used the PSP and the PANSS. Main outcome measure: Frequency with which each of the potential scoring inconsistencies occurs. Statistical analysis: Descriptive analysis of frequency of occurrence of each of the potential scoring inconsistencies. Trials or investigators will not be identified in the reporting of the results.

Brief Project Background and Statement of Project Significance: 

Symptom manifestations in schizophrenia can be subtle. Small imprecisions in measurement can lead to over- or under-estimation of change. One strategy to improve measurement is to conduct logical consistency checks between item responses (i.e., cross-sectionally) and across test administrations (i.e., longitudinally) of rating scales, bearing in mind that some degree of inconsistency is to be expected due to subject-based variability. The International Society for Central Nervous System (CNS) Clinical Trials and Methodology (ISCTM) expert Working Group focusing on improving consistency in measurement has been developing algorithms for flags to identify possible errors in use of rating scales widely used in our field. The model includes developing an algorithm based on the “expert opinion” of the working group and then testing it in data sets. Recommendations have been published for Positive and Negative Syndrome Scale (PANSS) (Rabinowitz, Schooler, et al 2017) and Montgomery-Asberg Depression Rating Scale (MADRS) (Rabinowitz, Schooler et al, 2019).
The current focus is the Personal and Social Performance (PSP).

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

Data on all subjects with available data on PSP domain and total scores, PANSS item level data, Clinical Global Rating of severity (CGI-s) data by visit with sequential subject identifiers.
Please note that I do not need patient demographic, psychiatric history, or safety data nor subject id numbers.

Narrative Summary: 

Symptom manifestations in studies of psychiatric disorders can be subtle. Small imprecisions in measurement can lead to over- or under-estimation of change. One strategy to improve measurement is to conduct logical consistency checks within and across measures. The International Society for CNS Clinical Trials and Methodology convened an expert working-group that assembled consistency/inconsistency flags for the Personal and Social Performance Scale for the purpose of improving the quality of measurement when using this scale. Flags will be applied to assessments derived from clinical trials to help understand how often various potential scoring inconsistencies occur.

Project Timeline: 

Once I obtain data sets I will apply consistency flags to the data sets. Data management and analysis are anticipated to take 4 months. The descriptive analysis emanating from this work will be included in a manuscript presenting the consistency flags . I anticipate the production of a manuscript for publication within 12 months.

Dissemination Plan: 

As previously done for the PANSS (Rabinowitz,et al, 2017) and MADRS (Rabinowitz et al, 2019) the goal is to produce a journal manuscript presenting the inconsistency flags for the PSP, the frequency of their occurrence in available data and recommendations. Target journal: Schizophrenia Research.

Bibliography: 

Rabinowitz J, Schooler NR, Anderson A, et al. Consistency checks to improve measurement with the Positive and Negative Syndrome Scale (PANSS). Schizophr Res. 2017;190:74–76. doi:10.1016/j.schres.2017.03.017

Rabinowitz J, Schooler NR, Brown B, Dalsgaard M, Engelhardt N, Friedberger G, et al. Consistency checks to improve measurement with the Montgomery-Asberg Depression Rating Scale (MADRS). J Affect Disord. 2019;256:143-7. https://doi.org/10.1016/j.jad.2019.05.077

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

Frequency of occurrence of potential scoring inconsistency based on expert consensus derived inconsistency flags.

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

There is no independent variable for this project. Potential scoring inconsistencies are defined based on the inconsistency flags developed by the expert working group of the International Society for CNS Clinical Trials and Methodology. The inconsistency flags are attached to this proposal.

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

N/A

Statistical Analysis Plan: 

New variables identifying inconsistencies in a given rating will be added to the data set. Each flag will be a new variable (See attached inconsistency flags). Using descriptive analysis, the data will be analyzed to examine the frequency with which each of the the potential inconsistencies occurs. Number of times that each flag occurs will be examined as will the number of times multiple flags occur in a single rating occur.

How did you learn about the YODA Project?: 
Software Used: 
R
Associated Trials: 
<ol><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/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></ol>
Make Publicly Available : 

2019-4075

Project Title: 
Derivation and Validation of Predictive Models in an aggregated Pulmonary Arterial Hypertension cohort
Specific Aims of the Project: 

Aim 1: Develop harmonized dataset of clinical trial data and perform feature selection on harmonized data for outcomes of interest (mortality, hospitalization, clinical worsening)
The objective of this Aim is to create a large patient-level dataset for modeling. The hypothesis is that use of a combined dataset will enable inclusion of a larger feature set for modeling and better predictive power from the resulting model, compared to using one trial dataset. Please see Statistical Analyses Plan for further details.

Aim 2: Develop and validate a predictive model for each outcome using machine learning and traditional methods
The objective of this aim is to develop predictive models that perform better than the current risk scores in both internal and external validation, as measured by the area under the receiver operating characteristic curve (AUC ROC). The secondary objective is that the predictive models are able to stratify patients by risk level, as measured by Kaplan-Meier curves. See SAP for more details.

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

Application Status

Approved Pending DUA Signature
Scientific Abstract: 

Background: There is a need for accurate prognostic tools to guide timely and effective therapies for patients with pulmonary arterial hypertension (PAH). Current PAH predictive models are limited due to small derivation populations and traditional modeling methods.
Objective: We will create a harmonized data set from clinical trial data in which to derive and validate predictive models for PAH patient survival, hospitalization, and clinical worsening outcomes.
Study Design: Patient mortality, hospitalization, and clinical worsening outcomes will be evaluated for PAH patients, with the goal of developing predictive models for each outcome. Clinical trial data will be combined into a harmonized dataset, creating a robust source for model derivation and validation.
Participants: This is a study of all Group 1 PAH patients in the requested clinical trials. Inclusion Criteria: Group 1 PAH only, and right heart catheterization diagnostic of pre-capillary pulmonary hypertension.
Main Outcome Measures: Outcomes of interest are patient mortality events, hospitalization events, and clinical worsening events. Models to predict these outcomes will be evaluated by the area under the receiver operating characteristic curve (AUC ROC). The success of stratifying patients by risk outcomes will be demonstrated by Kaplan Meier curves.
Statistical Analysis: Models will be derived using machine learning methods in R. Derived models will be validated on the harmonized data and on datasets outside of Yoda (Registry to Evaluate Early And Long-term PAH Disease Management (REVEAL), etc.)

Brief Project Background and Statement of Project Significance: 

Pulmonary Arterial Hypertension (PAH) is a chronic and rapidly progressive disease characterized by extensive narrowing of the pulmonary vasculature leading to progressive increases in pulmonary vascular resistance and eventual death. Approximately half a million persons in the USA will develop PAH over the course of their lifetime. Idiopathic PAH (IPAH) has survival rates at 1, 3, and 5 years of 68%, 48%, and 34%, respectively, with an average survival from onset of symptoms of 2.8 years if left untreated (Hyduk et al. 2005, Sitbon et al. 2002, D’Alonzo et al. 1991)
Even with treatment, the effective 5-year survival is only ~60% among those with PAH enrolled in the Registry to EValuate Early & Long Term PAH Disease Management (REVEAL) (Benza et al. 2012). PAH therapy using initial combination therapies is showing promise in the literature (Ghofrani and Humbert 2014, Galie et al 2009). However, the growing number of options creates a dilemma for determining which treatment is best for a particular patient. This is further complicated by the significant heterogeneity among patients with respect to their clinical responses to available therapies. Therefore, there is a critical need for an accurate, patient-specific prognostic tool to permit tailored, timely, targeted and effective therapies in PAH.
This research proposal is to develop a novel, machine-learning-enabled risk scores for patient risks of mortality, hospitalization, and clinical worsening. This effort builds off the development of the REVEAL and REVEAL 2.0 risk scores (Benza et al, 2010 and Benza et al 2019) by using clinical trial data harmonized to form a larger patient population than currently available in the REVEAL registry.
The resulting models will be made available in a web application for use by clinicians to guide patient treatment decision making.

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

Data Source: Requested clinical trials: COMPASS 2&3, BENEFIT
This is a study of all Group 1 PAH/ CTEPH patients in the requested clinical trials.
Inclusion Criteria:
• Group 1 PAH
• Right Heart Catheterization (RHC) at enrollment diagnostic of pre-capillary Pulmonary Hypertension (PH) (ie. Mean Pulmonary Arterial Pressure (MPAP) >25mmHg, Pulmonary Capillary Wedge Pressure (PCWP) <15mmHg)
Exclusion:
• Group 2-5 PH
• Baseline RHC data not meeting hemodynamic criteria (ie. MPAP <25 or PCWP >15)
• Insufficient data points more than 80% missing data
• Dropped out of clinical trial for reasons other than death or transplant

Narrative Summary: 

Stratifying risk for pulmonary arterial hypertension (PAH) patients is becoming critical to inform prognosis and guide treatment choice. Traditional treatment selection relied on clinical gestalt, but the need for an objective risk assessment to guide treatment is supported by expert consensus and research in recent years. Our proposed study will use clinical trial data to derive and validate a novel model for PAH mortality and morbidity predictions. This model will be derived using machine learning methods, including Bayesian modeling, a method that captures the interaction of multiple patient features are they relate to outcomes.

Project Timeline: 

Analysis and pre-processing of the data will take place immediately upon receipt of data access (estimated January 1.) The completion of the harmonization across clinical trials will be completed by the end of January, along with descriptive statistics on the data in the trials. The model derivation will take until the end of December, as multiple modeling modalities will be explored. Once the model is complete, validation will begin on the internal data (February) and external data. Updates to the model may be made throughout this time, with all data analysis and modeling complete by the end of March 2020. Results from analysis will be reported back to YODA by April 30, 2020. The following 3 months will be dedicated to manuscript preparation and writing, as well as abstract submission for CHEST and/or American Heart Association (AHA) conferences. The manuscript submission is expected for June 30th.

Dissemination Plan: 

The resulting product will be three predictive models for the three outcomes of interest. These models will be made available for patient risk stratification through the app.myphora.org web platform. The derivation and internal validation of these models will be published in a technical journal and the clinical implications of the models will be published in a clinical journal. The external validation of these models will be published in subsequent paper in a clinical journal.
Target audiences for this work are clinicians, policy makers, and bio-informaticists.

Clinical journals for publication: AHA, CHEST, Circulation
Technical journals for publication: IEEE, AMIA, AIMBE

We also plan to submit segments of the work as abstracts to clinical conferences (AHA, CHEST, PHA, ERS).

Bibliography: 

1. Hyduk A, Croft JB, Ayala C, Zheng K, Zheng Z-J, Mensah GA. Pulmonary hypertension surveillance: United states, 1980-2002. US Department of Health and Human Services; 2005.
2. Sitbon O, Humbert M, Nunes H, Parent F, Garcia G, Hervé P, Rainisio M, Simonneau G. Long-term intravenous epoprostenol infusion in primary pulmonary hypertension prognostic factors and survival. Journal of the American College of Cardiology. 2002;40:780-788
3. D'Alonzo GE, Barst RJ, Ayres SM, Bergofsky EH, Brundage BH, Detre KM, Fishman AP, Goldring RM, Groves BM, Kernis JT. Survival in patients with primary pulmonary hypertension results from a national prospective registry. Ann Intern Med. 1991;115:343-349
4. Benza RL, Miller DP, Barst RJ, Badesch DB, Frost AE, McGoon MD. An evaluation of long-term survival from time of diagnosis in pulmonary arterial hypertension from the reveal registry survival from time of diagnosis in reveal registry. CHEST Journal. 2012;142:448-456
5. Ghofrani H-A, Humbert M. The role of combination therapy in managing pulmonary arterial hypertension. European Respiratory Review. 2014;23:469-475
6. Galiè N, Negro L, Simonneau G. The use of combination therapy in pulmonary arterial hypertension: New developments. European Respiratory Review. 2009;18:148-153
7. Benza RL, Miller DP, Frost A, Barst RJ, Krichman AM, McGoon MD. Analysis of the lung allocation score estimation of risk of death in patients with pulmonary arterial hypertension using data from the reveal registry. Transplantation. 2010;90:298-305
8. Benza RL, Gomberg-Maitland M, Elliott CG, Farber HW, Foreman AJ, Frost AE, McGoon MD, Pasta DJ, Selej M, Burger CD, Frantz RP. Predicting Survival in Patients with Pulmonary Arterial Hypertension. CHEST. Online February 14, 2019

What is the purpose of the analysis being proposed? Please select all that apply.: 
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: 

Outcomes of interest:
• Mortality events
• Hospitalization events
• Clinical worsening events, as defined by:
1) Death, Transplantation, Hospitalization due to PH worsening (adjudicated), or Initiation of Prostanoid Therapy/Chronic Oxygen
2) Or, Disease progression defined as all three events occuring:
a. 15% decrease in six minute walk distance (6MWD) from baseline, confirmed by a second walk test on another day
b. A worsening of World Health Organization (WHO) Functional Class (FC) from baseline
c. Addition of a new PAH treatment

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

This assessment will use a combination of independent variables to predict the outcomes listed above (mortality, hospitalization, and clinical worsening) for patients with Group 1 PAH. These independent variables will be from the following categories, as available from each requested clinical trial:
• Laboratory values
• Demographics
• Functional Capacity
• Imaging (Electrocardiogram)
• Hemodynamics and Vitals
• Medical History

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

WHO category will be used to group patients for evaluating sub-populations within the Group 1 PAH cohort.
Patient enrollment in the clinical trial as placebo or intent to treat groups will be used to compare the model performances across groups.

Statistical Analysis Plan: 

The clinical trial data will be analyzed by a multi-step process:
• Data will be harmonized into a single dataset, encompassing the requested clinical trials
• Descriptive statistics will be evaluated for all variables
• Variables missingness will be calculated to chose threshold for inclusion and/or imputation
• Variables will be assessed for relationship to outcome of interest using Cox Regression analysis
• Selected variables will be included in predictive modeling for time to outcome using several modeling methodologies in R:
 Random Forest
 Bayesian Network Modeling
 Logistic Regression
 Neural Networks
• Resulting models will be internally evaluated with a 20% withheld test set for performance, as measured by the area under the receiver operating characteristic curve and calibration curve
• Sub-groups in the analysis will be compared by Kaplan-Meier curves to determine significance of risk stratification
 Subgroups may be: WHO category, gender, age group, race, risk scores
• Resulting models will be externally validated on registry data including but not limited to: REVEAL, COMPERA, French PH registry, and the PHSANZ registry
• Resulting models will be externally validated on clinical data from other clinical trials, including by not limited to: AMBITION, FREEDOM-EV, GRIPHON
• Models developed on other PH datasets will be validated in the harmonized dataset

Data harmonization plan:
Data harmonization efforts will focus on unifying key identified features per a previously conducted meta-analysis. Two time points for patients will be considered for harmonization: baseline values and their initial 12-16 week follow-up. Model training will be conducted on two specific endpoints: death (follow-up/last known status) and clinical worsening (end of study). Model training will initially optimize for short term prediction (e.g. one-year survival from baseline and initial follow-up, one-year clinical worsening from baseline and initial follow-up) to maximize the available number of patients with a known status. For early withdrawal/censored patients, status will be imputed as “alive” or “event-free”, which has been demonstrated in literature to be a robust imputation strategy provided that early withdrawal patients only compose 20% of the training population. When longer term prediction is desired, inverse proportional censor weighting can be applied to re-create a “pseudo-population” by replicating patients with long-term known status to effectively replace patients who are censored at an earlier time point, maintaining the same overall population survival curve. This weighting can be determined by modeling the trial itself as a causal effect of early censoring, such that the pseudo-population will then no longer depend on trial follow-up time. Contingency plans will include building temporal models such that early censored patients can still contribute to an accumulation of evidence up until their time of censor.

As part of our ongoing project PHORA, we have developed and validated a risk stratification tool using machine learning to compliment and enhance the traditional methods of analysis (which are typically Cox’s proportional hazard models). We analyzed data of REVEAL 2.0 registry to develop a BN risk model using the variables found in the REVEAL 2.0 calculator with the same discretization cut points at baseline presentation. We then validated it in external registries (e.g. COMPERA) to ensure its validity by creating KM curves separating patients into low, intermediate and high risk based on the 2015 ESC/ ERS guidelines (i.e., low-risk <5% ; intermediate-risk 5%–10%; high-risk >10% 12-month mortality).
As a next step, we are ‘updating’ PHORA to allow it to capture additional clinical variables from contemporary PAH clinical trials (such as those being requested from Yoda). As the model learns from a larger number , it will undergo 10-fold cross validation which will be reported as ROC-AUC.

How did you learn about the YODA Project?: 
Software Used: 
R
Associated Trials: 
<ol><li><a href="/node/3740">NCT01106014 - AC-065A302 - A Multicenter, Double-blind, Placebo-controlled Phase 3 Study Assessing the Safety and Efficacy of Selexipag on Morbidity and Mortality in Patients With Pulmonary Arterial Hypertension</a></li><li><a href="/node/3910">NCT00303459 - AC-052-414 (COMPASS-2) - Effects of Combination of Bosentan and Sildenafil Versus Sildenafil Monotherapy on Morbidity and Mortality in Symptomatic Patients With Pulmonary Arterial Hypertension - A Multicenter, Double-blind, Randomized, Placebo-controlled, Parallel Group, Prospective, Event Driven Phase IV Study</a></li><li><a href="/node/3911">NCT00433329 - AC-052-419 - COMPASS 3: An Open-label, Multi-Center Study Employing a Targeted 6-Minute Walk Test (6-MWT) Distance Threshold Approach to Guide Bosentan-Based Therapy and to Assess the Utility of Magnetic Resonance Imaging (MRI) on Cardiac Remodeling</a></li><li><a href="/node/3912">NCT00091715 - AC-052-364 - A Randomized, Double-blind, Placebo-controlled, Multicenter Study to Assess the Efficacy, Safety, and Tolerability of Bosentan in Patients With Mildly Symptomatic Pulmonary Arterial Hypertension (PAH)</a></li><li><a href="/node/3913">NCT00660179 - AC-055-302 - A Multicenter, Double-blind, Randomized, Placebo-controlled, Parallel Group, Event-driven, Phase III Study to Assess the Effects of Macitentan (ACT-064992) on Morbidity and Mortality in Patients With Symptomatic Pulmonary Arterial Hypertension</a></li></ol>
Make Publicly Available : 

2019-4071

Project Title: 
Efficacy and safety of SGLT2 inhibitors in the treatment of type 2 diabetes: systematic review incorporating unpublished clinical study reports
Specific Aims of the Project: 

The relevant research question for this doctoral project is as follows: How can clinical study reports, as an unpublished source of evidence, be used to evaluate novel drug treatments used in chronic conditions to inform prescribing and healthcare spending decisions?
The overarching aim of this project is to explore the role of clinical study reports as unpublished sources of randomised control trial reporting in evidence synthesis for Canagliflozin / SGLT2s and its impact on estimates of effectiveness and safety. This project’s aims will be achieved by focusing on the selected medication in terms of safety and efficacy and CSR methodology.

What type of data are you looking for?: 
Full CSR
Associated Trial(s): 

Application Status

Approved Pending DUA Signature
Scientific Abstract: 

Background: The prevalence of symptomatic type 2 diabetes in the Irish population is approximately 5.2%, a figure which has more than doubled over the course of almost 20 years.
Objective: The objectives of this project are outlined here. Objective 1: To synthesise all available randomised control trial evidence on the efficacy and safety of Canagliflozin / SGLT2s and to determine the impact of using clinical study reports on the estimated clinical outcomes. Objective 2: To document procedures for identifying, accessing, extracting data from, and analysing CSRs for research purposes. Objective 3: To evaluate the differences between published sources and clinical study reports in terms of reporting outcomes & bias.
Study Design: Systematic Review and meta-analysis using both published and unpublished sources.
Participants: The population of interest will be adults with type 2 diabetes.
Main outcome measures: Particular outcomes of interest will include change in HbA1 and specific clinical efficacy and safety outcomes relevant to diabetes.
Statistical analysis: A narrative synthesis of included studies will be conducted. Meta-analysis will be undertaken where possible for included efficacy and safety outcomes using appropriate random effects regression models. We would plan to perform a network metanalysis contingent on access to CSR related data for other agents in the same class.

Brief Project Background and Statement of Project Significance: 

Background: Type 2 diabetes is a significant public health burden with an associated reduced life expectancy, increased mortality and is associated with greater risk of other diseases including cancer, cognitive impairment and arthritis.
The Sodium-glucose co-transporter 2 (SGLT2) inhibitors are a novel group of medications used in diabetes. There are four SGLT2s currently licenced for use in the EU; Dapagliflozin, Empagliflozin, Ertugliflozin and Canagliflozin.
Obtaining a more complete picture of the evidence base for these medications will involve a thorough systematic review and meta-analysis of all available RCTs, including both published sources and also unpublished sources, namely clinical study reports (CSRs). The importance of including such unpublished evidence in decision making for medications has been illustrated in a number of studies and systematic reviews
Clinical Study Reports (CSRs) are documents submitted by drug companies to organisations such as the European Medicines Agency (EMA) to evaluate new medications. They include information on the data and statistics involved in drug trials for these medications. When compared to information published in the usual medical journals, CSRs provide richer information on these trials and more detailed results. CSRs are becoming much more widely available so it is vital for researchers to be able to analyse these lengthy documents and draw conclusions from the information. This will hopefully provide greater knowledge on medications and improve decision making for medications which will ultimately benefit patients.
In this project, it is planned that CSRs will be analysed for a novel medication group called the SGLT2 inhibitors (SGLT2s) which are licensed for use in type 2 diabetes. Information on the patient benefits and harms will be obtained from the CSRs. A thorough review of all published information on these medications will also be obtained and compared with that in the CSRs. It is planned to study the impact of CSR information on the areas of (i) Patient benefits and harms (ii) Appraisal of methodological quality.
Following this, it may also be possible to use these estimates of treatment effects to perform an economic evaluation of these medications. This would hopefully highlight the importance of using CSRs for costing of medications by decision makers. The overall aim of this project is to see whether any additional information held in the CSRs could improve our knowledge and improve decision making for medications.

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

All relevant randomised control trials on Canagliflozin / SGLT2s will be considered, mainly phase 3 trials with phase 2b trials being considered if appropriate. In terms of patient characteristics, the population of interest will be adults with type 2 diabetes. The intervention will be the use of medication Canagliflozin / SGLT2s. The control will be usual care with an active comparator, or placebo.

A conventional systematic review of publised RCTs related to the SGLT2 inhibitors Dapagliflozin, Canagliflozin, Ertugliflozin and Empagliflozin will be carried out using traditional medical databases for systematic reviewing. In addition CSRs for trials related to these medications will be requested from YODA Project (Canagliflozin) and from the European Medicines Agency.

Narrative Summary: 

In this project, it is planned that CSR data for the SGLT2 inhibitors, which include canagliflozin, will be incorporated into a thorough systematic review of these medications. A thorough review of all benefits and harms on this medication will be compiled from all published sources, as well as from CSRs and trial registries. All data will be analysed and compared. It is planned to study the impact of CSR information on clinical outcomes and also appraise the methodological quality of these documents. The overall aim of this project is to see whether any additional information held in the CSRs could improve our knowledge and improve decision making for these medications.

Project Timeline: 

The project is proposed to begin in earnest in January 2020 with a full systematic review and meta-analysis to be carried out. It is anticipated that analysis will be completed within a year of this date (January 2021) and the results of the study submitted for publication within a further year after this. Results can be reported back to the YODA Project at this time.

Dissemination Plan: 

It is planned that the protocol for the relevant systematic review for this medication will be published on PROSPERO. Following completion of this doctoral project it is planned that the results of each relevant part will be published in high impact peer reviewed journals to ensure that the results of the study are appropriately disseminated. It is envisaged that this will include results on the impact of CSR-informed estimates on efficacy and safety, as well as on cost-effectiveness. Specific target journals of interest include the British Medical Journal, BMJ Evidence Based Medicine, BMJ Open, BMC Series and JAMA Internal Medicine as a number of similar relevant papers in relation to clinical study reports have been published through these media previously.

Bibliography: 

1. Doshi P, Jefferson T. Clinical study reports of randomised controlled trials: an exploratory review of previously confidential industry reports. BMJ open. 2013;3(2).
2. Doshi P, Jefferson T, Del Mar C. The imperative to share clinical study reports: recommendations from the Tamiflu experience. PLoS Med. 2012;9(4):e1001201.
3. Turner EH, Matthews AM, Linardatos E, Tell RA, Rosenthal R. Selective publication of antidepressant trials and its influence on apparent efficacy. The New England journal of medicine. 2008;358(3):252-60.
4. Jefferson T, Jones M, Doshi P, Spencer EA, Onakpoya I, Heneghan CJ. Oseltamivir for influenza in adults and children: systematic review of clinical study reports and summary of regulatory comments. Bmj. 2014;348:g2545.
5. Heneghan CJ, Onakpoya I, Thompson M, Spencer EA, Jones M, Jefferson T. Zanamivir for influenza in adults and children: systematic review of clinical study reports and summary of regulatory comments. Bmj. 2014;348:g2547.
6. Jefferson T, Jones MA, Doshi P, Del Mar CB, Hama R, Thompson MJ, et al. Risk of bias in industry-funded oseltamivir trials: comparison of core reports versus full clinical study reports. BMJ open. 2014;4(9):e005253.
7. Wieseler B, Kerekes MF, Vervoelgyi V, McGauran N, Kaiser T. Impact of document type on reporting quality of clinical drug trials: a comparison of registry reports, clinical study reports, and journal publications. Bmj. 2012;344:d8141.
8. Doshi P, Jefferson T. Open data 5 years on: a case series of 12 freedom of information requests for regulatory data to the European Medicines Agency. Trials. 2016;17:78.
9. Davis AL, Miller JD. The European Medicines Agency and Publication of Clinical Study Reports: A Challenge for the US FDA. Jama. 2017;317(9):905-6.
10. Zinman B, Wanner C, Lachin JM, Fitchett D, Bluhmki E, Hantel S, et al. Empagliflozin, Cardiovascular Outcomes, and Mortality in Type 2 Diabetes. The New England journal of medicine. 2015;373(22):2117-28.
11. la Cour JL, Brok J, Gotzsche PC. Inconsistent reporting of surrogate outcomes in randomised clinical trials: cohort study. Bmj. 2010;341:c3653.
12. Donnan JR, Grandy CA, Chibrikov E, Marra CA, Aubrey-Bassler K, Johnston K, et al. Comparative safety of the sodium glucose co-transporter 2 (SGLT2) inhibitors: a systematic review and meta-analysis. BMJ open. 2019;9(1):e022577.

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 that confirms or validates previously conducted research on treatment safety
Research on clinical trial methods
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

Particular outcomes of interest will include change in HbA1c, all-cause mortality, reduction in micro- and macro-vascular complications, patient-reported symptoms and quality of life and specific adverse outcomes reported for Canagliflozin / SGLT2s including renal impairment, diabetic ketoacidosis, volume depletion, fournier's gangrene and lower limb amputation. The outcomes of note will be both clinically significant and significant from the point of view of patients. Any other relevant outcomes will also be included.

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

The above outcomes will be analysed with particular focus on patient related outcomes. All relevant clinical efficacy and safety outcomes will be considered. In terms of treatment effect measures, relative risk (RR) will be used for dichotomous data (e.g. cardiovascular mortality) and mean difference or standardised mean difference for continuous data. For event rate data (e.g. numbers of hospitalisations) incidence rate ratio will be used and for time-to-event data (e.g. time to first hospitalisation) hazard ratio will be used.

Statistical Analysis Plan: 

A narrative synthesis of included studies will be conducted. Data extraction will be performed separately for clinical study reports and published trial reports. In terms of effect measure of interest, the relative risk for pre-specified clinical outcomes of interest will be analysed. Meta-analysis will be undertaken where possible for included efficacy and safety outcomes using appropriate random effects regression models (treatment effect varying across studies). We would plan to perform a network metanalysis contingent on access to CSR related data for other agents in the same class.
Meta-analyses will be conducted using all available sources (to include CSR-based sources), as well as just with published sources. In cases of discrepancy between trial registry and publication, sensitivity analysis using publication information will also be conducted. Heterogeneity in outcomes due to study characteristics will be evaluated using meta-regression, if sufficient studies are identified.
With regard to examining the methodology and quality of CSR-based trials compared with published trials as outlined above, outcomes will be summarised across CSRs and published sources and differences will be assessed using appropriate statistical test for paired data (i.e. paired t test, Wilcoxon test, McNemar test).

How did you learn about the YODA Project?: 
Software Used: 
STATA
Associated Trials: 
<ol><li><a href="/node/307">NCT00642278 - 28431754DIA2001 - A Randomized, Double-Blind, Placebo-Controlled, Double-Dummy, Parallel Group, Multicenter, Dose-Ranging Study in Subjects With Type 2 Diabetes Mellitus to Evaluate the Efficacy, Safety, and Tolerability of Orally Administered SGLT2 Inhibitor JNJ-28431754 With Sitagliptin as a Reference Arm</a></li><li><a href="/node/308">NCT01106625 - 28431754DIA3002 - A Randomized, Double-Blind, Placebo-Controlled, 3-Arm, Parallel-Group, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control on Metformin and Sulphonylurea Therapy</a></li><li><a href="/node/309">NCT01064414 - 28431754DIA3004 - A Randomized, Double-Blind, Placebo-Controlled, 3-Arm, Parallel-Group, 26-Week, Multicenter Study With a 26-Week Extension, to Evaluate the Efficacy, Safety and Tolerability of Canagliflozin in the Treatment of Subjects With Type 2 Diabetes Mellitus Who Have Moderate Renal Impairment</a></li><li><a href="/node/310">NCT01081834 - 28431754DIA3005 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin as Monotherapy in the Treatment of Subjects With Type 2 Diabetes Mellitus Inadequately Controlled With Diet and Exercise</a></li><li><a href="/node/311">NCT01106677 - 28431754DIA3006 - A Randomized, Double-Blind, Placebo and Active-Controlled, 4-Arm, Parallel Group, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control on Metformin Monotherapy</a></li><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><li><a href="/node/313">NCT01106651 - 28431754DIA3010 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin Compared With Placebo in the Treatment of Older Subjects With Type 2 Diabetes Mellitus Inadequately Controlled on Glucose Lowering Therapy</a></li><li><a href="/node/314">NCT01106690 - 28431754DIA3012 - A Randomized, Double-Blind, Placebo-Controlled, 3-Arm, Parallel-Group, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control on Metformin and Pioglitazone Therapy</a></li><li><a href="/node/315">NCT01137812 - 28431754DIA3015 - A Randomized, Double-Blind, Active-Controlled, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin Versus Sitagliptin in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control on Metformin and Sulphonylurea Therapy</a></li><li><a href="/node/2416">NCT01809327 - 28431754DIA3011 - A Randomized, Double-Blind, 5-Arm, Parallel-Group, 26-Week, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin in Combination With Metformin as Initial Combination Therapy in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control With Diet and Exercise</a></li><li><a href="/node/2421">NCT01381900 - 28431754DIA3014 - A Randomized, Double-Blind, Placebo-Controlled, Parallel Group, 18-Week Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control on Metformin Alone or in Combination With a Sulphonylurea</a></li><li><a href="/node/2431">NCT01340664 - 28431754DIA2003 - A Randomized, Double-Blind, Placebo-Controlled, 3-Arm, Parallel-Group, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control on Metformin</a></li><li><a href="/node/2436">NCT02025907 - 28431754DIA4004 - A Randomized, Double-blind, Placebo Controlled, 2-arm, Parallel-group, 26-week, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control on Metformin and Sitagliptin Therapy</a></li><li><a href="/node/3982">NCT01032629 - 28431754DIA3008 - A Randomized, Multicenter, Double-Blind, Parallel, Placebo-Controlled Study of the Effects of JNJ-28431754 on Cardiovascular Outcomes in Adult Subjects With Type 2 Diabetes Mellitus</a></li><li><a href="/node/3983">NCT01989754 - 28431754DIA4003 - A Randomized, Multicenter, Double-Blind, Parallel, Placebo-Controlled Study of the Effects of Canagliflozin on Renal Endpoints in Adult Subjects With Type 2 Diabetes Mellitus</a></li></ol>
Make Publicly Available : 

2019-4067

Project Title: 
Evidence-generation for biologics in pediatric studies
Specific Aims of the Project: 

The specific aims of the project are:
(i) to identify early treatment endpoints supporting shorter duration of RCTs in pediatric populations and quantify the extent to which these endpoints can approximate gold standard long-term endpoints
(ii) to evaluate the feasibility of projecting treatment effects on pediatric populations based on EHR data and RCTs conducted in adult populations.
The overarching goal is to develop new statistical tools to make evidence-generation for biologics in pediatric populations more efficient and feasible and validate the proposed tools using the RCT data requested through YODA and EHR data from Boston Children’s Hospital (BCH).

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

Approved Pending DUA Signature
Scientific Abstract: 

Background: With the emergence of biologics over the past 15 years, substantial advances have been made in the treatment of a number of pediatric diseases. However, evidence-generation for these new therapies is challenging due to issues in conducting randomized clinical trials (RCT) in children. New statistical tools to improve the efficiency and feasibility of evidence-generation in pediatric studies are needed.
Objective: The project aims to develop new statistical tools to evaluate the efficacy of biologics in pediatric populations by leveraging both RCT and observational data.
Study Design: We will use data from requested RCTs and electronic health record (EHR) data from local clinical institutions to develop and validate new methods. Specifically, we will develop new statistical tools to (i) identify and evaluate early treatment endpoints supporting shorter duration of RCTs in pediatric populations; (ii) project treatment effects on pediatric populations based on the relevant EHR data and RCTs conducted in adults.
Participants: All enrolled patients in the requested trials.
Main Outcome Measures: For each requested pediatric study, we will report (i) the identified early treatment endpoint and the proportion of treatment effect the identified early treatment endpoint can explain; (ii) the treatment effect in children projected from the relevant EHR data and RCTs conducted in adults.
Statistical Analysis: We will develop new statistical methods for evidence-generation in pediatric studies, and apply and validate the proposed methods using the requested studies.

Brief Project Background and Statement of Project Significance: 

The impact of pediatric drug therapies has dramatically changed over the past 15 years with the emergence of biologics. For example, there is a growing interest in the development and use of anti-tumor necrosis factor (anti-TNF) therapy in children (McCluggage, 2011). Whereas treatment used to aim for reduction of symptoms in certain conditions such as inflammatory bowel disease, anti-TNF therapy can help heal the mucosa, eliminate symptoms, and modify the natural course of the disease. Anti-TNF therapy is of potential benefit in many pediatric diseases. In 2014, the U.S. Food and Drug Administration (FDA) approved adalimumab, an anti-TNF agent, for the treatment of pediatric patients with Crohn’s disease after the initial approval of adult patients in 2007 based on follow-up clinical studies confirming the safety and efficacy of adalimumab in pediatric patients in 2012 (Patel et al., 2016). In general, many substantial advances have been made in pharmacological therapy in pediatric populations and Rose (2019) showed that FDA reviewed 130 pediatric drug therapies from 2007 to 2011.
How to efficiently evaluate the safety and efficacy of new therapies such as biologics in pediatric patients is a critical question with substantial impacts on the drug development and regulatory process for children. Most biologic therapies used in pediatric populations, including adalimumab, are initially studied in adults, and often lack sufficient evidence to support their efficacy in pediatric patients. For example, infliximab, another anti-TNF therapy, was FDA-approved for use in adult patients with rheumatoid arthritis in 1999. Since then, despite some evidence indicating it is efficacious and safe in juvenile rheumatoid arthritis (JRA) patients, infliximab has still not been approved by the FDA for use in JRA due to a lack of sufficient evidence (Stall and Cron, 2014).
Although randomized controlled trials (RCTs) remain the gold standard for drug evidence-generation, solely relying on RCTs to evaluate drug safety and efficacy is often not feasible for pediatric populations due to a number of disincentives and ethical challenges (McMahon and Dal Pan (2018)). Barriers to conducting pediatric trials include small patient populations with slow and costly trial accrual, liability and complex ethical issues related to testing products in vulnerable patient populations, practical challenges in obtaining consent and conducting trials in children (e.g. need for pediatric drug formulations), and lack of validated pediatric assessment tools and clinical end-points. In addition, in children, long-term follow-up is often required to assess treatment effects across multiple stages of development and to measure adverse events related to growth and development. This type of follow up is often impractical and costly. As a result, there are substantial gaps in evidence on the safety and efficacy of many newly developed biologic drugs in children as in the example of infliximab.

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

We will request pediatric RCTs studying biologics and the relevant adult RCTs through YODA. For each RCT we request, we aim to use individual-level participant data (IPD) with demographic and clinical baseline information, treatment information as well as clinical outcomes including treatment response. For example, we wish to collect all the subcomponents of American College of Rheumatology (ACR) score in the requested JRA studies so that we can easily change from ACR score to other outcome measures used in JRA, such as Juvenile Arithmetic Disease Activity Scores (JADAS), in our study if necessary.
In our study, we will also include EHR data for the observational pediatric cohort relevant to the pediatric populations studied in the requested RCTs and treated at Boston Children’s Hospital (BCH). These data will be obtained separately through our local institutions.
The YODA data and the EHR data may be stored in their own respective servers. To integrate information from the two data sources, we will derive summary-level data from YODA such as regression coefficients and predicted treatment response curve given a propensity score of response as detailed in the statistical plan.

Narrative Summary: 

The project aims to develop new statistical tools to support evaluations of the efficacy of biologics in pediatric patients and make evidence-generation in pediatric studies more efficient and feasible.

Project Timeline: 

The project is expected to be completed in a year:
1. Months 1-4: data collection
2. Months 5-18: data analysis and method development
3. Months 9-24: Manuscript preparation and publication (2-3 anticipated publications)
We will share all manuscripts generated using YODA data at the time of submission with the YODA project team.

Dissemination Plan: 

Our work will be disseminated through 2-3 scientific publications in statistics and medicine, such as the Journal of the American Statistical Association and JAMA Pediatrics. We will also present the work at national conferences, such as the Joint Statistical Meeting. Statistical software for implementing the proposed methodologies will also be distributed to the research community. In addition, we plan on engaging with a number of stakeholders in pediatric studies throughout the project, including pharmaceutical companies, regulatory agencies, and patient stakeholders to both inform our work and develop work products addressing the needs of these specific groups. For example, in collaboration with the FDA, our methods could contribute to regulatory guidance on how to accelerate drug development for pediatric populations with the proposed more efficient and feasible evidence-generation procedure.

Bibliography: 

1. McCluggage, L. K. (2011). Safety of TNF inhibitors in adolescents and children. Adolescent health, medicine and therapeutics, 2, 1.
2. Patel, A. S., Suarez, L. D., & Rosh, J. R. (2016). Adalimumab in pediatric Crohn's disease. Immunotherapy, 8(2), 127-133.
3. Rose, K. (2019). Challenges in Pediatric Drug Development. Pediatric Drugs, 11(1), 57-59.
4. Stoll, M. L., & Cron, R. Q. (2014). Treatment of juvenile idiopathic arthritis: a revolution in care. Pediatric rheumatology, 12(1), 13.
5. McMahon, A. W., & Dal Pan, G. (2018). Assessing drug safety in children—the role of real-world data. The New England journal of medicine, 378(23), 2155.
6. X. Wang, L. Parast, L. Tian & T. Cai (2019) Model-Free Approach to Quantifying the Proportion of Treatment Effect Explained by a Surrogate Marker. Biometrika, 2019, accepted.
7. Zhang, Z., Nie, L., Soon, G., & Hu, Z. (2016). New methods for treatment effect calibration, with applications to non‐inferiority trials. Biometrics, 72(1), 20-29.
8. Elze, M. C., Gregson, J., Baber, U., Williamson, E., Sartori, S., Mehran, R., ... & Pocock, S. J. (2017). Comparison of propensity score methods and covariate adjustment: evaluation in 4 cardiovascular studies. Journal of the American College of Cardiology, 69(3), 345-357.

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
Develop or refine statistical methods
Research on clinical trial methods
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

For each pediatric study of biologics we request, the main outcome measures will be:
1. the identified early treatment endpoint and the proportion of treatment effect on the gold standard long-term endpoint the identified early treatment endpoint can explain (PTE);
2. the projected treatment effect based on the relevant EHR data and potentially RCTs conducted in adults.

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

None

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

None

Statistical Analysis Plan: 

We will develop statistical methods to make evidence-generation of biologics in pediatric studies more feasible and efficient.
Objective 1. To identify and evaluate early treatment endpoints supporting shorter duration of pediatric RCTs, we will develop robust model-free statistical methods to identify surrogate endpoints and quantify its degree of surrogacy. Surrogate endpoints to be considered include both qualitative measurements of treatment response and event time outcomes such as progression free survival at an earlier time (Wang et al., 2019). Candidate surrogate endpoints will be selected in collaboration with a pediatric rheumatologist. We will develop methods that allow us to estimate the degree of surrogacy using EHR data, and validate the proposed methods using the long-term endpoints in RCT data for the pediatric studies of biologics we are requesting.
The proposed analysis does not require individual-level data transportation across servers where RCT data and EHR data are stored. In particular, we will only use EHR data to identify the surrogate endpoint, and only use RCT data for validation.
Objective 2. To evaluate the feasibility of projecting treatment effects on pediatric populations based on EHR data and RCTs conducted in adults, we will develop transfer learning methods to predict causal treatment effects for pediatric populations. In particular, we aim to first use observational EHR data to derive a model for predicting how patient characteristics such as age, gender, disease severity measures as well as comorbidities affect the treatment effect. We will use the model to derive a scoring system that assigns patients into different subgroups with potentially different levels of treatment benefit. Then we will develop a robust causal inference procedure to infer about causal treatment effects for each subgroup using EHR data by modeling how covariates affect both the propensity score and the outcome within each subgroup. The same scoring system will be applied to the adult RCT data to estimate the subgroup specific causal treatment effect. The estimated subgroup specific treatment effects from EHR and from RCT will be combined to produce a final estimate of the treatment effect for a target pediatric population with a specific distribution of the baseline characteristics (Zhang et al., 2016; Elze et al., 2017). We will validate the proposed estimate via data integration and transfer learning by assessing the consistency between the projected treatment effect from our method and the treatment effect estimated from the gold standard RCTs for the pediatric studies we request.
The above proposed analysis does not require sharing individual-level data between RCTs and EHR. The model development of the scoring system only uses EHR data and the estimated model coefficients will be uploaded to the platform where RCT data are stored. The subgroup specific treatment effect estimate takes a form of a univariate function that maps a univariate score to an estimated treatment effect. This estimated function, which is a summary-level result, will be derived from RCT and then sent to the EHR data site. Similarly, for the final validation analyses, only estimated functions and model parameters will be transported between platforms.

How did you learn about the YODA Project?: 
Software Used: 
R
Associated Trials: 
<ol><li><a href="/node/155">NCT00036374 - C0168T32 - A Randomized, Double-Blind Trial of Anti-TNF Chimeric Monoclonal Antibody (Infliximab) in Combination With Methotrexate for the Treatment of Patients With Polyarticular Juvenile Rheumatoid Arthritis</a></li><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/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/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/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/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/356">NCT00269867 - C0168T22 - A Placebo-Controlled, Double-Blinded, Randomized Clinical Trial of Anti-TNF Chimeric Monoclonal Antibody (cA2) in Patients With Active Rheumatoid Arthritis Despite Methotrexate Treatment</a></li><li><a href="/node/357">NCT00236028 - C0168T29 - A Randomized, Double-blind, Trial of Anti-TNFa Chimeric Monoclonal Antibody (Infliximab) in Combination With Methotrexate Compared With Methotrexate Alone for the Treatment of Patients With Early Rheumatoid Arthritis</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/558">NCT01009086 - CNTO1275PSA3001 /// PSUMMIT I - A Study of the Safety and Effectiveness of Ustekinumab in Patients With Psoriatic Arthritis</a></li><li><a href="/node/559">NCT01077362 - CNTO1275PSA3002 /// PSUMMIT II - A Study of the Safety and Efficacy of Ustekinumab in Patients With Psoriatic Arthritis With and Without Prior Exposure to Anti-TNF Agents</a></li><li><a href="/node/755">NCT01551290 - CR018769; REMICADEUCO3001 - 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/989">NCT00732875 - P05645 - A Placebo-controlled, Double-blinded, Randomized Clinical Trial of Anti-TNF Chimeric Monoclonal Antibody (cA2) in Korean Patients With Active Rheumatoid Arthritis Despite Methotrexate Treatment (Open-label Extension Part)</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/1686">NCT00267969 - C0743T08 - A Phase 3, Multicenter, Randomized, Double-blind, Placebo Controlled Trial Evaluating the Efficacy and Safety of Ustekinumab (CNTO 1275) in the Treatment of Subjects With Moderate to Severe Plaque-type Psoriasis </a></li><li><a href="/node/1696">NCT00307437 - C0743T09 - A Phase 3, Multicenter, Randomized, Double-blind, Placebo-controlled Trial Evaluating the Efficacy and Safety of CNTO 1275 in the Treatment of Subjects With Moderate to Severe Plaque-type Psoriasis</a></li><li><a href="/node/2951">NCT00207714 - C0524T02 - A Randomized, Double-blind, Dose-ranging Trial of CNTO 148 Subcutaneous Injection Compared With Placebo in Subjects With Active Rheumatoid Arthritis Despite Treatment With Methotrexate</a></li><li><a href="/node/2996">NCT00202852 - P04280 - A Placebo-Controlled, Double-Blinded, Randomized Clinical Trial of Anti-TNF Chimeric Monoclonal Antibody (cA2) in Korean Patients With Active Rheumatoid Arthritis Despite Methotrexate</a></li><li><a href="/node/3026">C0168T14 - Therapeutic efficacy of multiple intravenous infusions of anti-tumor necrosis factor alpha monoclonal antibody combined with low-dose weekly methotrexate in rheumatoid arthritis</a></li><li><a href="/node/3366">NCT01550744 - CNTO1275PSO3009 - A Phase 3b, Randomized, Double-blind, Active-controlled, Multicenter Study to Evaluate a "Subject-tailored" Maintenance Dosing Approach in Subjects With Moderate-to-Severe Plaque Psoriasis</a></li><li><a href="/node/3371">NCT02203032 - CNTO1959PSO3003 - A Phase 3, Multicenter, Randomized, Double-blind Study to Evaluate the Efficacy and Safety of Guselkumab for the Treatment of Subjects With Moderate to Severe Plaque-type Psoriasis and an Inadequate Response to Ustekinumab</a></li><li><a href="/node/3376">NCT00723528 - JNS009-JPN-02 - A Placebo-Controlled Double-Blind Comparative Study of CNTO1275 in Patients With Plaque Type Psoriasis</a></li><li><a href="/node/3386">NCT00320216 - C0379T04 - A Phase II, Randomized, Double-blind, Placebo-controlled, Parallel Study of Single and Multiple Dose Regimens With Subcutaneous CNTO 1275 (Human Monoclonal Antibody to IL-12) in Subjects With Moderate to Severe Psoriasis</a></li><li><a href="/node/3391">NCT00454584 - C0743T12 - A Phase 3, Multicenter, Randomized Study Comparing CNTO 1275 and Etanercept for the Treatment of Moderate to Severe Plaque Psoriasis</a></li><li><a href="/node/3396">NCT00747344 - C0743T25 - A Phase 3, Multicenter, Randomized, Double-blind, Placebo-controlled Study Evaluating the Efficacy and Safety of Ustekinumab in the Treatment of Korean and Taiwanese Subjects With Moderate to Severe Plaque-type Psoriasis</a></li><li><a href="/node/3406">NCT01008995 - C0743T23 - A Phase 3, Multicenter, Randomized, Double-blind, Placebo-controlled Study Evaluating the Efficacy and Safety of Ustekinumab in the Treatment of Chinese Subjects With Moderate to Severe Plaque-type Psoriasis</a></li><li><a href="/node/3411">NCT01059773 - CNTO1275PSO4004 - An Exploratory Trial to Assess Naturalistic Safety and Efficacy Outcomes in Patients With Moderate to Severe Plaque Psoriasis Transitiioned to Ustekinumab From Previous Methotrexate Therapy (TRANSIT)</a></li><li><a href="/node/3421">NCT01090427 - CNTO1275PSO3006 - A Phase 3 Multicenter, Randomized, Double-blind, Placebo-controlled Study Evaluating the of Efficacy and Safety of Ustekinumab in the Treatment of Adolescent Subjects With Moderate to Severe Plaque-type Psoriasis (CADMUS)</a></li><li><a href="/node/3506">NCT01230827 - CNTO148JIA3001 - A Study of the Safety and Efficacy of CNTO 148 (Golimumab) in Children With Juvenile Idiopathic Arthritis (JIA) and Multiple Joint Involvement Who Have Poor Response to Methotrexate (GO KIDS)</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/3536">NCT01962974 - CNTO148ART3003 - A Golimumab Phase 3b, Multicenter, Assessment of Intravenous Efficacy in Rheumatoid Arthritis Subjects Who Have Diminished Disease Control Despite Treatment With Infliximab (REMICADE®)</a></li><li><a href="/node/3626">NCT00036387 - C0168T41 - A Randomized, Double-blind Trial of the Safety of Anti-TNF Chimeric Monoclonal Antibody (Infliximab) in Combination With Methotrexate Compared to Methotrexate Alone in Patients With Rheumatoid Arthritis on Standard Disease-modifying Anti-Rheumatic Drug </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 : 

2019-4066

Project Title: 
Assessment of treatment outcome with golimumab in nr-axSpA by baseline CRP
Specific Aims of the Project: 

To determine the (1) ASAS40 response (primary outcome), and also examine secondary outcomes (2) ASAS20, (3) ASAS partial remission, (4) BASDAI50, (5) change in SPARCC MRI SI score, (6) change in BASDAI and (7) change in ASDAS in patients with nr-axSpA to golimumab by their baseline CRP.

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

Application Status

Approved Pending DUA Signature
Scientific Abstract: 

Background:
Golimumab has been trialled in the study in question and found to be effective in patient with non-radiographic axial spondyloarthritis. Studies on golimumab and other TNF inhibitors have shown TNF to be a important response criteria (1-4). However the clinical trial data does not specify response rates beyond positive and negative. The purpose of the study is to explore the response rates with differing levels of CRP. This will enable clinicians to make chooses about therapy for patients knowing their likely response rate based on their actual CRP level.
Objective:
To determine the ASAS40 response rate (and other secondary outcomes) in patients with non-radiographic axial spondyloarthritis by different CRP levels.
Study Design:
A secondary analysis of the primary trial results to determine the ASAS40 response rates (and other secondary outcomes) by different CRP levels. The individual patient level data will be divided into the CRP groups and the primary and secondary outcomes analysed.
Participants:
All participants in the primary clinical trial.
Main Outcome Measure(s):
Primary outcome: ASAS40 at 16 weeks by different CRP levels.
Secondary outcomes: Other secondary outcomes measured in the trial at 16 weeks by different CRP levels (both dichotomous and continuous).
Statistical Analysis:
Descriptive statistics and logistic regression.

Brief Project Background and Statement of Project Significance: 

Non-radiographic axial spondyloarthritis (nr-axSpA) is an inflammatory arthritis affecting the spine and sacroiliac joints. Effective therapies are available, including golimumab, which is part of the anti-tumour necrosis factor (TNF) class. Previous work has demonstrated that predictors of response to anti-TNF agents include a raised CRP. However the level of raised CRP that predicts a response to golimumab has not been determined.
This project is significant because often patients with nr-axSpA have low positive CRP levels and the level at which patients will respond to anti-TNFs like golimumab is not know. This work has the potential to provide significant guidance to practising clinicians on when it is best to commence agents like golimumab with the expectation that they will respond. Therefore this work can provide guidance to clinicians on when golimumab use would be of limited value and when it would be of good value to the patient (and secondarily to the payer).

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

The data source would be the participants data from the clinical trial in question.
All participants in the trial would be included in this analysis.

Narrative Summary: 

TNF inhibitors have been trialled in non-radiographic axial spondyloarthritis (nr-axSpA), a type of inflammatory arthritis that affects the spine and sacroiliac joints, and found to be effective. Just like in ankylosing spondylitis a blood marker of inflammation called C-reactive protein (CRP) has been found to predict response to treatment with TNF inhibitors. The published manuscript on the trial of golimumab in nr-axSpA reports response by positive CRP or negative CRP. The aim of this research is to analyse the trial in more detail to determine what the cut-points is for response to enable doctors to use this information in their clinical practice to better select patients for treatment.

Project Timeline: 

From receiving the data it will take approximately 3-4 months to complete the analysis, another 1 month to draft the manuscript, and then submission after this. This project is part of a larger project to assess CRP responses across a number of biologics and so it may be the case that the analysis on this YODA data is completed prior to the other data being available and the aim is to include all data in the same manuscript. All manuscripts will be shared with YODA at the time of submission.

Dissemination Plan: 

Posters at scientific meetings, will submit to the American College of Rheumatology annual scientific meeting and/or the European League against Rheumatism annual scientific meeting. Published in a peer reviewed scientific journal of high quality, depending on the final manuscript, the target journal will be Annals of the rheumatic diseases, Arthritis & Rheumatology, Arthritis Care & Research, Rheumatology (Oxford) or Arthritis Research & Therapy.

Bibliography: 

1. Brown, M.A., et al., Evaluation of the effect of baseline MRI sacroiliitis and C reactive protein status on etanercept treatment response in non-radiographic axial spondyloarthritis: a post hoc analysis of the EMBARK study. Ann Rheum Dis, 2018. 77(7): p. 1091-1093.
2. Sieper, J., et al., A randomized, double-blind, placebo-controlled, sixteen-week study of subcutaneous golimumab in patients with active nonradiographic axial spondyloarthritis. Arthritis Rheumatol, 2015. 67(10): p. 2702-12.
3. Dougados, M., et al., Symptomatic efficacy of etanercept and its effects on objective signs of inflammation in early nonradiographic axial spondyloarthritis: a multicenter, randomized, double-blind, placebo-controlled trial. Arthritis Rheumatol, 2014. 66(8): p. 2091-102.
4. Sieper, J., et al., Efficacy and safety of adalimumab in patients with non-radiographic axial spondyloarthritis: results of a randomised placebo-controlled trial (ABILITY-1). Ann Rheum Dis, 2013. 72(6): p. 815-22.

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: 

ASAS40 outcome measure will be the primary outcome measure used, this is a well recognised and widely used outcome measure in clinical trials of axial spondyloarthritis. The ASAS response criteria are used to assess improvement in axial spondyloarthritis in clinical trials. Each of four domains is scored by the patient on a visual analog scale ranging from 0 to 10. The four domains are as follows:

1. Patient global assessment of disease activity for the past week
2. Patient assessment of back over the past week
3. Function (BASFI)
4. Inflammation (severity and duration of morning stiffness)

An ASAS40 response is defined as an improvement of at least 40% and an absolute improvement of at least 1 unit (on a 0-10 scale) in at least three of four domains, with no worsening of the remaining domain. ASAS20 is the same but only a 20% improvement. ASAS partial remission, BASDAI50, Change in SPARCC MRI SI score and change in BASDAI are all widely used outcome measures in rheumatology, the character restriction prevents me from describing them fully.

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

CRP level:
(a) <5 mg/L
(b) 5-10mg/L
(c) > 10mg/L
It will be defined as the measures CRP in the clinical trial in question both as dichotomous groups and as a continuous measure.

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

All variables were defined in the original study. The ASAS response criteria are used to assess improvement in axial spondyloarthritis in clinical trials. Each of four domains is scored by the patient on a visual analog scale ranging from 0 to 10. The four domains are as follows:

1. Patient global assessment of disease activity for the past week
2. Patient assessment of back over the past week
3. Function (BASFI)
4. Inflammation (severity and duration of morning stiffness)
An ASAS40 response is defined as an improvement of at least 40% and an absolute improvement of at least 1 unit (on a 0-10 scale) in at least three of four domains, with no worsening of the remaining domain. In each CRP group, there will be a proportion who reach the ASAS40 response, this is the main outcome measure. ASAS partial remission, BASDAI50, Change in SPARCC MRI SI score and change in BASDAI are measured and defined in the original study and I will be simply examining their relationship to dichotomous CRP groups and CRP as a continuous measure.

Statistical Analysis Plan: 

ASAS40 responses are defined as a composite (see above). Once each patient has had their ASAS response calculated (see above), then each CRP group will have a proportion that reach an ASAS40 response, this is the primary outcome measure. The process will be completed for each outcome measure, ie. in each CRP subgroup the outcome measures will be examined, and descriptive analyses generated to try to make a comparison between different CRP subgroups. The study was not designed or powered to look at these subgroups so making formal statistical comparisons may be of limited value but logistic regression analysis will be used to examine for the relationship of CRP to these outcome measures.

How did you learn about the YODA Project?: 
Software Used: 
R
Associated Trials: 
<ol><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></ol>
Make Publicly Available : 

2019-4018

Project Title: 
Confirming the psychometric properties of the PANSS-6 and the outcome relationships with quality of life and cognitive functioning.
Specific Aims of the Project: 

PANSS-6 and PANSS-30 scores will be analysed to determine if the PANSS-6 is a valid and reliable tool at predicting symptom severity and outcomes in the cognitive and functioning domains. It may be necessary to determine if there are other, more suitable, items in the PANSS-30 that are better suited in the shortened version to effectively show symptom severity and predictive outcomes. Primary analysis has already been completed on FEP (n=169) and TRS (n=72) patients. We seek further data pools to increase our sample size and further validate our findings.
The purpose of assessing the functional outcomes is to determine whether or not there is a predictive relationship between symptoms and the effect on functioning, and whether or not this changes when using PANSS-6 vs PANSS-30 rating scales.
Aim and Hypothesis:
1. Psychometric properties of the PANSS-6
a. Are the PANSS-6 and PANSS-30 scalable measures of the symptom severity of treatment resistant schizophrenia?
b. Does total scores on PANSS-6 correlate with total scores on PANSS-30 in TRS and FEP?
c. Is PANSS-6 is a useful substitute for functioning and quality of life, similar to that of PANSS-30.
2. Is there a correlation between symptoms severity, quality of life and functioning, and cognition.

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

Incomplete Not Reviewed
Scientific Abstract: 

The PANSS-30 is one of the most widely used rating scale in schizophrenia. However, it takes up to 1 hour to administer, so, there is a need to develop a shorter rating scale. Six of the PANSS-30 items (P1-Delusions, P2-Conceptual disorganization, P3-Hallucinations, N1-Blunted Affect, N4-Social Withdrawal, N6-Lack of Spontaneity and flow of conversation) have been identified as scalable, and thus accurately able to represent symptom severity1. Although there is evidence to show that these six items provide unique information regarding symptom severity, this has not been compared in first episode psychosis (FEP) as against treatment resistant schizophrenia (TRS).
We set out to test the validity and scalability of the six-item version (PANSS-6) of the 30-item PANSS in these populations. We assessed the functional outcomes of functioning and cognition to determine whether or not there is a predictive relationship between symptoms and the effect of functioning and cognition, and whether or not this changes when using the PANSS-6 vs PANSS-30 rating scales.
We combined data from three studies investigating FEP (n=169) and TRS (n=72). All participants had a primary diagnosis of Schizophrenia or Schizophrenia Spectrum Disorder, as initially determined by the referring team, and then confirmed via a clinical interview.
We investigated the following: (i) the scalability of PANSS-6 and PANSS-30; (ii) the correlation between PANSS-6 and PANSS-30 total scores; (iii) and any group differences between FEP and TRS. We

Brief Project Background and Statement of Project Significance: 

The PANSS-30 is the most widely used rating scale in schizophrenia1. Due to the substantial time it takes to administer the PANSS-30 and the burden it places on the individual there is a need for a shorter rating scale in the use of research and clinical settings2. Ostergaard set out to show that a shorter version of the PANSS-30 could provide the same, scalable, results as the full version of PANSS-30. They showed that 6 items subtracted from the PANSS-30, specifically P1-Delusions, P2-Conceptual disorganization, P3-Hallucinations, N1-Blunted Affect, N4-Social Withdrawal, N6-Lack of Spontaneity and flow of conversation, was scalable. That is, Ostergaard identified the PANSS-6 as a scalable rating scale measuring the same latent dimension over time and across patients, as well as being able to define short-term remission. They have shown these results to be stable across in-patients with acute episodes of schizophrenia3, outpatients with chronic schizophrenia4 and treatment-resistant schizophrenia5. This study sets out to further validate these findings across FEP and TRS.
Level of functioning and quality of life constitute further important outcome criteria for understanding the complex nature of schizophrenia6. Researchers have found that the PANSS-6 is a useful substitute for functioning and quality of life, similar to that of PANSS-307. People with schizophrenia commonly report having a lower quality of life than the general population. Poor quality of life, or functional capacity, is the inability to function in the real world and in schizophrenia it produces the burden of disease. The most commonly reported domains that are impaired in this population are vocation, everyday living skills and interpersonal relations. There are a number of self-report rating scales that are used to assess quality of life. The functioning of the TRS population was assessed using the Manchester Short Assessment of Quality of Life (MANSA) and Assessment of Quality of Life (AQoL), and the FEP population was assessed using the Social Functioning Scale (SFS).
Furthermore, there is a gap between functional capacity and real world functioning and it has been found by Cardenas et al8 that self-efficacy, i.e. motivation, is an important factor in understanding why some individuals have the capacity to function well but do not translate this in to real-world functioning. Self-efficacy was assessed in the TRS sample using the Revised Self-Efficacy Scale (RSES). Additionally, there is a considerable impairment in cognition in people with schizophrenia. The MATRICS battery9, is an eight domain cognitive battery that has been constructed to examine the major cognitive impairments reported in psychosis and related conditions.
There is a well-established link between symptom severity (PANSS-30 scores) and functioning (QoL scores) and cognition. That is those with lower functioning scores and lower cognitive ability generally score higher on the PANSS-30. It is important for a shorter rating scale to correctly identify this relationship. The author seeks to validate the findings from Ostergaard and his colleagues to show that scalable and valid results can be ach

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

For the preliminary analysis we pooled together data from three studies completed at the Brain Dynamics Centre, Westmead Institute for Medical Research, with Prof Anthony Harris as the primary investigator. All three studies utilised the PANSS-30 for the assessment of clinical symptoms. The EHANCE clinical trial, as mentioned above, assessed 72 people with TRS. The other two studies; the General and Emotional Cognition in First Onset Psychosis (GEM) and the Western Sydney First Episode Psychosis Project (WSFEP) tested 169 participants with first episode psychosis. We are seeking a larger data sample to that meet the following criteria
Inclusion Criteria
• Ages 12 – 65
• Confirmed primary diagnosis of schizophrenia or Schizophrenia Spectrum Disorder
• Currently experiencing symptoms, PANSS-30 score >60
• Ability to consent
Exclusion criteria:
• Inability or unwillingness to provide consent
• Illicit substance dependence-
• Mental retardation
• Medical condition or disease that might interfere with the assessments (e.g. hearing impairment)
• Neurological disorder (e.g. epilepsy)
• Disagreement to abide by participation and preparation requirements.
• Not English literate

Narrative Summary: 

The Positive and Negative Syndrome Scale (PANSS) is a commonly used tool in research and clinical practice for diagnosing schizophrenia. The scale consists of 30 rating domains providing measure of psychosis, taking 1 hour to administer it places a considerable burden on the individual and clinician. Recent research has indicated that a shorter version of the PANSS-30, provides comparable information to the full PANSS-30. We set out to validate these findings. Using data from three studies, the full PANSS-30 scores will be compared against the shorter 6 item PANSS looking at psychometric properties and its predictive ability for measures of functioning, quality of life, and cognition.

Project Timeline: 

Preliminary statistical analysis will be completed by the beginning of November 2019, with an abstract written and ready for presentation at the Society for Mental Health Research (SMHR) at the end of November 2019, presented by Ms Hansen. Following this a manuscript will be drafted by the end of December 2019 and first submitted for publication February 2020. This data will be a part of Ms De Wet’s Clinical Master’s thesis expected to be completed by the end of 2020.

Dissemination Plan: 

If our findings are significant we anticipate that our findings will reach the psychiatric clinical and research population. We are targeting publication in the following journals: Schizophrenia Bulletin, Neuroscience & Biobehavioral Reviews and The Royal Australian and New Zealand College of Psychiatrists (RANZCP).

Bibliography: 

1. Leucht, S. Measurements of response, remission and recovery in schizophrenia and examples for their clinical application. J. Clin Psychiatry 2014; 75 (Suppl 1): 8-14).
2. Correll, C.U., Kishimoto, T., Nielsen J., Kane, J.M. Quantifying clinical relevance in the treatment of schizophrenia. Clin Ther 2011;33:B16-B39.
3. Østergaard, S.D, Lemming, O.M., Mors, O., Correll, C.U., Bech P. PANSS-6: a brief rating scale for the measurement of severity in schizophrenia. Acta Psychiatr Scand 2016; 133: 436-444.
4. Ostergaard, S.D., Foldager, L., Mors, O., Bech, P., Correll, C.U. The validity and sensitivity of PANSS-6 in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) Study. Schizophrenia Bulletin; 2018a; 44(2): 453-462.
5. Ostergaard, S.D., Foldager, L., Mors, O., Bech, P., Correll, C.U. The validity and sensitivity of PANSS-6 in treatment-resistant schizophrenia. Acta Psychiatr Scand 2018b; 138: 420-431.
6. Andreasen, N.C., Carpenter W.T. Jr., Jane, J.M., Lasser R.A., Marder, S.R., Weinberger, D.R. Remission in schizophrenia proposed criteria and rationale for consensus. Am J Psychiatry. 2005;162:441-449.
7. L. Hochstrasser1, S. Borgwardt1, M. Lambert2, B. G. Schimmelmann3,4, U. E. Lang1, R.-D. Stieglitz1,5, C. G. Huber1
8. Cardenas, V., Abel, S., Bowie, C.R., tizando, D., Depp, C.A., Patterson, T.L., Jeste, D.V., Mausbach, B.T. When functional capacity and real-world functioning converge: the role of self-efficacy. Schizophrenia Bulletin, 2013;39(4):908-916.
9. Kern RS, Nuechterlein KH, Green MF, Baade LE, Fenton WS, Gold JM. The MATRICS Consensus Cognitive Battery, part 2: co-norming and standardization. American Journal of Psychiatry. 2008;165(2):214-20.

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: 

The main outcome of this study is to determine if the PANSS-6 is a valid and sensitive tool at predicting symptom severity, as well as quality of life and cognitive outcomes, in comparison to the PANSS-30. Once the scalability of the PANSS-6 is determined, we will then run a series of correlations to determine if the PANSS-6 is capable at predicting functional outcomes.

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

An Item-Response Theory (IRT) analysis will be conducted in R to determine if the 6 items identified by Ostergaard and colleagues are scalable and provide enough information to accurately determine symptom severity in FEP and TRS. If it is shown that the 6 items previously identified are not scalable then we will suggest other items from the PANSS-30 that are scalable as shown through the IRT.

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

Our preliminary findings on FEP (n=169) and TRS (n=72) showed there was a strong relationship between the PANSS-6 and PANSS-30 scores for people with TRS (Spearman’s rho= .768, p=0.000), this was not found in FEP (Spearman’s rho= -.055; p=0.479). Further work will analysis this correlation on an item level; that is are there specific PANSS items that are more predictive of a stronger relationship in treatment groups.
It is expected that there will be a relationship between PANSS-30 and lower scores on QoL, self-efficacy and cognition. In order to determine if the PANSS-6 is a valid tool it is important to show that this relationship exists also, which will be assessed through Pearson correlation coefficients, and subscales will be assessed through Phi coefficients.

Statistical Analysis Plan: 

1) Scalability of the PANSS-6 and PANSS-30
a. Rasch rating scale model – invariant ordering of items such that some items have higher prevalence and lower severity, and others have higher severity and lower prevalence (null hypothesis). Null hypothesis tested with X2 statistics. If X2 is low and the P-value is consistently >0.01, then the null hypothesis is not rejected and the scale is accepted as being scalable.
b. Differential item functioning (DIF) analysis– to test whether age sex time or treatment allocation influenced the item rank order. The P-value >0.01 indicated that the Rasch model for DF was not rejected.
2) Correlation between PANSS-6 and PANSS-30
a. Spearman correlation analysis – total scores of PANSS-6 and PANSS-30; correlation between relative change in total score
3) Comparison of QoL subscales - Phi coefficients
4) Correlation of QoL scales with PANSS – Pearson correlation coefficient
5) Correlation of QoL scales with cognition scores? Pearson correlation coefficient

How did you learn about the YODA Project?: 
Software Used: 
R
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/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/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/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/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/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/562">NCT00216476 - RISSCH3001 - CONSTATRE: Risperdal® Consta® Trial of Relapse Prevention and Effectiveness</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/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/860">NCT00253136 - RIS-USA-121/CR006055 - Risperidone Depot (Microspheres) vs. Placebo in the Treatment of Subjects With 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/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/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/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/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></ol>
Make Publicly Available : 

2019-4013

Project Title: 
Prognostic relevance of prior local therapy, time to metastasis and time to castration resistant prostate cancer (CRPC) in metastatic CRPC (mCRPC)
Specific Aims of the Project: 

SPECIFIC AIMS:
1. To determine the proportion of chemo-naïve mCRPC patients with PLT (RP +/- RT or RT) treated with abiraterone/placebo + prednisone in the COU-AA-302 trial.
2. To determine the number of patients that were diagnosed of mCRPC by PSA progression or by radiographic progression (with or without PSA progression).
3. To determine the number of patients with newly diagnosed mCRPC who were metastatic at diagnosis, metastatic before CRPC or metastatic after CRPC.
To evaluate:
1. To evaluatethe impact of PLT on OS in abiraterone + prednisone and placebo + prednisone treated patients with chemonaïve mCRPC
2. To evaluate the impact of time to metastasis and time to CRPC on OS in abiraterone + prednisone and placebo + prednisone treated patients with chemonaïve mCRPC
SPECIFIC HYPOTHESES:
We hypothesized that PLT, time to metastasis and time to CRPC could be prognostic factors on OS in chemotherapy naïve mCRPC patients.

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: 

Brackground: The role of prior local therapy (PLT) on the outcomes of men with metastatic prostate cancer (PC) is a subject of growing interest. Retrospective studies have suggested an improved survival in metastatic hormone sensitive prostate cancer (mHSPC) patients who underwent PLT with radical prostatectomy (RP) or radiation therapy (RT). The effect of PLT in newly diagnosed mCRPC patients and its prognostic significance remains unknown.
Objective: to evaluate the incidence and prognostic significance of PLT with RP or RT, time to metastasis and time to CRPC on overall survival (OS) in chemo-naïve mCRPC patients treated with AA/placebo + prednisone
Study design: retrospective cohort study
Participants: mCRPC patients in the COU-AA-302 trial
Main Outcome Measures: Overall survival
Statistical Analysis: Patients will be categorised based on PLT (PR or RT or both). OS will be estimated by the Kaplan-Meier method. Cox proportional hazards regression models will be used to test the association of PLT, time to metastasis and time to CRPC with OS.

Brief Project Background and Statement of Project Significance: 

Despite the decrease in incidence of metastases at PC diagnosis from over 50% in the 1970s to currently less than 10%, the population of patients with metastatic disease at diagnosis accounts for half of PC mortality (1). A median time to CRPC of 16 months and an OS of 5.2 years was observed in men with detectable metastasis at diagnosis that died of mCRPC. The other half of patients who died of mCRPC had localized PC at diagnosis (64% of which were classified as high-risk PC), with a median OS of 8.8 years (1). Data suggest that the notion of an initially indolent disease slowly progressing to the metastatic phase and death is valid in only approximately half of patients who die of PC.
While different retrospective studies have suggested a survival benefit of local therapy (cytoreductive prostatectomy or radiation therapy) in metastatic PC (2,3,4), the impact of PLT on OS in mCRPC has been scarcely addressed. One recent retrospective study conclude that men who progressed from non-metastatic CRPC (nmCRPC) to mCRPC and had undergone RP +/- RT for localised disease had improved survival compared with those who were treated with androgen deprivation therapy (ADT) alone. This survival benefit is not seen in men treated with RT alone. Selection of patients in this study was based on non‐metastatic CRPC, excluding patients with positive imaging for distant metastases before the CRPC diagnosis (5).

The impact of time to metastasis on OS in patients with PC has also been retrospectively studied. In a recent study, authors conclude that due to the shortest time from diagnosis to CRPC, patients with "de novo" metastasis have the worst OS compared with those who were free of metastasis initially but developed metastasis before or after becoming castration-resistant. Once CRPC was reached, no difference in survival time was observed between groups. Authors suggested that efforts aimed to prolong the development of CRPC seem to be essential to improve survival time (6).
Consequently, efforts must be aimed to:
1. Define the optimal treatment strategy for patients with upfront metastatic disease. Currently, the most controversial approach is the indication of treatment to the primary tumor. Recently a survival benefit was seen with RT to the primary tumor for newly diagnosed prostate cancer with a low metastatic burden (7). No evidence exists for surgery in this setting.
2. Identify patients with localised PC with a higher likelihood of dying of the disease, who could potentially benefit from alternative treatment modalities other than extended surgery and conventional adjuvant therapies. Novel endocrine therapies such as apalutamide, for instance, are being currently tested in the neoadjuvant setting.
We intend to evaluate the incidence of PLT with RP or RT and assess the impact on OS in mCRPC patients treated with AA/placebo + prednisone in the COU-AA-302 trial (8)

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

Data Source: COU-AA-302
Inclusion criteria: patients treated with abiraterone + prednisone and placebo + prednisone in the COU-AA-302 trial

Narrative Summary: 

Hormone therapy is the usual treatment for metastatic prostate cancer (cancer cells have spread to bones, lung or liver). After some months of this treatment, patients stop responding to hormone therapy. This condition is known as CRPC.
The aim of this study is to find if patients with metastatic CRPC who received treatment with surgery (radical prostatectomy) or radiation when they were first diagnosed of prostate cancer, do better than those treated initially with hormone therapy. Furthermore, the study will try to find if time to develop metastasis and time to CRPC diagnosis influence the outcomes of the patients.

Project Timeline: 

Project submission: december 2019
Contract: march-april 2019
Analysis: april-june 2020
Abstract submission (AUA 2021): september 2020
Paper draft circulation: october-november 2020
Paper submission: november-december 2020

Dissemination Plan: 

Abstract presentation in AUA 2021
Submission of manuscript first-quartile oncology journals: Annals of Oncology, European Urology, Clinical Cancer Research

Bibliography: 

1. Patrikidou A, Loriot Y, Eymard J-C, et al. Who dies of prostate cancer?. Prostate Cancer and Prostatic Diseases volume 17, pages348–352 (2014)
2. Reichard CA, Gregg JR, Achim MF et al. Radical prostatectomy in metastatic castration‐resistant prostate cancer: feasibility, safety, and quality of life outcomes. Eur. Urol. 2018; 74: 140–3.
3. Löppenberg B, Dalela D, Karabon P, et al. The impact of local treatment on overall survival in patients with metastatic prostate cancer on diagnosis: a national cancer data base analysis. Eur Urol, 72 (2017), pp. 14-19
4. Poelaert F, Verbaeys C, Rappe B et al. Cytoreductive prostatectomy for metastatic prostate cancer: first lessons learned from the multicentric prospective local treatment of metastatic prostate cancer (LoMP) trial. Urology 2017; 106: 146–52.
5. Patel DN, Jha S, Howard LE, et al. Impact of prior local therapy on overall survival in men with metastatic castration-resistant prostate cancer: Results from Shared Equal Access Regional Cancer Hospital. Int J Urol. 2018 Dec;25(12):998-1004.
6. Frees S, Akamatsu S, Bidnur S, et al. The impact of time to metastasis on overall survival in patients with prostate cancer. World J Urol. 2018 Jul;36(7):1039-1046.
7. Parker CC, James ND, Brawley CD, et al. Systemic Therapy for Advanced or Metastatic Prostate cancer: Evaluation of Drug Efficacy (STAMPEDE) investigators. Radiotherapy to the primary tumour for newly diagnosed, metastatic prostate cancer (STAMPEDE): a randomised controlled phase 3 trial. Lancet. 2018;392:2353-2366.
8. Ryan CJ, Smith MR, de Bono JS, et al. Abiraterone in Metastatic Prostate Cancer without Previous Chemotherapy. N Engl J Med 2013; 368:138-148

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: 

MAIN OUTCOME MEASURE:
Overall survival, defined as the time (months) from PC diagnosis to death
SECONDARY OUTCOME MEASURES:
- Radiographic progression-free survival (rPFS), which will be defined as the time from study trial treatment initiation to radiographic progression or death
- Clinical progression-free survival (cPFS), which will be defined as the time from time from study trial treatment initiation to clinical progression or death, in months.
- Time to quality of life deterioration will be defined as the time from study trial treatment initiation to clinically significant FACTP or BPI-SF progression.

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

- Prior local therapy (PLT), defined as radical prostatectomy (RP), radiotherapy (RT) or both as definitive treatment for localized disease. PLT will be categorized into two groups: RP +/- RT and RT alone.
-Time to metastasis, which will be defined as time (months) from PC diagnosis to develop of metastatic disease. They will be categorized in metastatic at diagnosis, metastatic before CRPC or metastatic after CRPC
-Time to CRPC, which will be defined as time (months) from start of ADT to CRPC.

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
- Presence of metastatic disease at diagnosis
- Type of metastatic disease at diagnosis

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.
- The Kaplan-Meier method will be used to estimate median survival times (OS, rPFS, cPFS, PSA-PFS, time to metastases, time to CRPC) and 95% confidence intervals, in months.
- Cox proportional-hazards (Cox-PH) models will be used to test the association of prior local therapy, metastatic disease at diagnosis, time to CRPC and time to metastases with overall survival (primary endpoint), radiographic progression-free survival, biochemical progression-free survival and clinical progression-free survival (secondary endpoints). Other covariates that show a significant (p<0.05) association with survival in the univariable Cox-PH model may be included in the multivariable Cox-PHmodel. 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.

How did you learn about the YODA Project?: 
Software Used: 
R
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: 
2020

2019-3999

Project Title: 
Psychiatric Symptoms as Adverse Events of Abiraterone / Prednisolone Therapy: Systematic Review and Meta-Analysis
Specific Aims of the Project: 

Aim of our project is to systematically investigate and review whether a therapy with Abiraterone / Prednisolone is associated with psychiatric adverse events. Product information mentions only non-psychiatric adverse events during a treatment with Abiraterone / Prednisolone. If we can support this with evidence, Abiraterone should be superior as older anti-androgens in patients with comorbid psychiatric disorder or a vulnerability towards a psychiatric disorder. Our hypothesis is that based on the occurrence of psychiatric symptoms in older anti-androgens, Abiraterone / Prednisolone should also cause psychiatric symptoms. To evaluate or hypothesis we will extract data on psychiatric (including psychosomatic) adverse events from interventional and observational studies using Abiraterone / Prednisolone, determine their frequencies across studies, and meta-analytically compare the frequencies of psychiatric adverse events during Abiraterone / Prednisolone to those during placebo.

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: Metastasized prostate carcinoma is one of the most frequent diseases of geriatric males. Among the treatment option are anti-androgens such as Flutamide, Enzelutamide, or Cyproteron acetate. These drugs have been frequently associated with psychiatric adverse events. The novel anti-androgen Abiraterone does not seem to cause psychiatric adverse events. Objective: The aim of our study is to systematically review all available studies employing Abiraterone in patients with metastasized prostate carcinoma, to extract data for psychiatric adverse events such as anxiety, depression, and insomnia, and to analyze both qualitatively and quantitatively whether or there is a particular risk for these events. Study Design: Our study is a systematic review and meta-analysis of both published and unpublished data on patients with prostate carcinoma who received anti-androgen therapy with Abiraterone. Studies are collected after a literature search in two scientific databases (PubMed, WebOfScience) and two Clinical Trial Registries (EudraVigilance, ClinicalTrials). Data from unpublished studies are sought out by contacting study authors or manufacturers. Total numbers of exposed patients and frequencies of psychiatric and psychosomatic adverse events of these patients are extracted, categorized, and reviewed. Participants: No one, because it is a Review. Main Outcome: Frequencies for psychiatric or psychosomatic adverse events for Abiraterone. Statistical Analysis: For placebo-controlled studies, meta-analyses are calculated for adverse events during Abiraterone vs. Placebo.

Brief Project Background and Statement of Project Significance: 

Abiraterone is a novel anti-androgen drug which is used in the treatment of metastasized prostate carcinoma. In contrast to older anti-androgens Abiraterone is not an antagonist on androgen receptors but rather inhibits the synthesis of androgens, estrogens, and glucocorticoids. As reported in product information, therapy with Abiraterone does not cause psychiatric or psychosomatic adverse events unlike the older anti-androgens. Our project is to evaluate the risk of psychiatric or psychosomatic by the means of a systematic review and meta-analysis. If the results of our independent study confirm the safety of Abiraterone regarding mental health, it may help raise awareness of the suitability of Abiraterone therapy in patients with prostate carcinoma and comorbid psychiatric disorders of vulnerability towards a psychiatric disorder.

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

For the literature search, we used two databases (PubMed/MEDLINE and WebOfScience) and two clinical trials registries (EudraVigilance and ClinicalTrials). Keyword for research was „Abiraterone“ without further restrictions. Selection of literature was performed manually using pre-defined criteria. We included observational and interventional trials on male, human subjects with prostate carcinoma with at least one treatment arm of Abiraterone / Prednisolone that reported at least one psychiatric adverse events.
Terms which represent psychiatric adverse events and we are interested in, are the following: "Asthenia", "Fatigue", "Anorexia", "Loss of Appetite", "Decreased Appetite", "Depression", "Insomnia", "Anxiety", "Somnolence", "Depressed Mood", "Delirium", "Confusional State", "Confusion", "Metal Status Change", "Hallucination", "Loss of Libido", "Decreased Libido", "Mood Swings", "Amnesia", and/or "Psychiatric Disorder".

Narrative Summary: 

Older anti-androgen treatment option haven been frequently associated with psychiatric adverse events such as depression, anxiety, and loss of libido. In contrast, the novel anti-androgen Abiraterone does not seem to cause psychiatric adverse events with none being reported in any of the product informations (Zytiga,Abiratas, Abretone, Abirapro). The aim of this study is to systematically review studies employing Abiraterone/Prednisolone in patients with prostatic cancer and to analyse whether or not there is a risk for psychiatric adverse events. Adverse events of interest include but are not limited to anxiety, depression, and insomnia.

Project Timeline: 

Our project is nearly finished. All aforementioned databases and clinical trial registries were searched and the studies were scanned, categorized and analyzed. Frequencies were extracted and evaluated. We also contacted several authors of studies that did not report adverse events in their publications. The requested studies in YODA is the last in our attempt to gain a most complete set of data.

Project start date: 01.04.2019
Analysis completion date: 15.10.2019 (but still waiting for additional and more detailed data from YODA)
Manuscript drafted date: 01.04.2019
Publication date: 01.06.2019
Results reported back date: 28.20.2020

Dissemination Plan: 

Our project is part of a master thesis for a M.Sc. in Psychology. A condensed version of the thesis is also going to be published in a journal focusing either on urology or geriatric psychiatry. Potential journals are: "Der Urologe (The Urologist)" and "Aktuelle Urologie (Actual Urology)". But we also want to try to publicate first in "The Uropean Journal of Urology".
If submission is succesfull you will receive a copy of the manuscript.

Bibliography: 

A short selected enumeration of included studies:

- de Bono et al. (2011) / NCT00638690
- Fizazi et al. (2017) / NCT01715285
- Ryan et al. (2013) / NCT00887198
- Ye et al. (2017) / NCT01591122
- Attard et al. (2018) / NCT01995513
- Clarke et al. (2018) / NCT01972217
- de Bono et al. (2019) / NCT01485861
- Smith et al. (2019) / NCT02043678
- Attard et al. (2019) / NCT01867710
- Chi et al. (2013) / NCT01681433
- Madan et al. (2012) / NCT01553188
- Stein et al. (2018) / NCT02737332
- Sydes et al. (2018) / NCT00268476
- Szmulewitz et al. (2018) / NCT01543776

What is the purpose of the analysis being proposed? Please select all that apply.: 
New research question to examine treatment safety
Research that confirms or validates previously conducted research on treatment safety
Summary-level data meta-analysis:
Summary-level data meta-analysis will pool data from YODA Project with other additional data sources
Participant-level data meta-analysis will pool data from YODA Project with other additional data sources
Other
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

Our study investigates frequencies of psychiatric adverse events during treatment with Abiraterone / Prednisolone in all included studies. All randomized, placebo-controlled studies will be part of meta-analysis.

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

For the review, the predictor for the outcome of interest is treatment with Abiraterone / Prednisolone. For the meta-analysis, the predictor for the outcome of interest is treatment (with Abiraterone / Prednisolone vs. Placebo).
Terms which represent psychiatric adverse events and we are interested in to conduct the frequencies of, are the following: "Asthenia", "Fatigue", "Anorexia", "Loss of Appetite", "Decreased Appetite", "Depression", "Insomnia", "Anxiety", "Somnolence", "Depressed Mood", "Delirium", "Confusional State", "Confusion", "Metal Status Change", "Hallucination", "Loss of Libido", "Decreased Libido", "Mood Swings", "Amnesia", and/or "Psychiatric Disorder".

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

Frequencies of the following specified psychiatric advers events symptoms: "Asthenia", "Fatigue", "Anorexia", "Loss of Appetite", "Decreased Appetite", "Depression", "Insomnia", "Anxiety", "Somnolence", "Depressed Mood", "Delirium", "Confusional State", "Confusion", "Metal Status Change", "Hallucination", "Loss of Libido", "Decreased Libido", "Mood Swings", "Amnesia", and/or "Psychiatric Disorder".

Statistical Analysis Plan: 

In the systematic review, data will be analyzed qualitatively. In the meta-analysis, odds-ratios will be calculated for the different adverse effects occurring under Abiraterone / Prednisolone vs. Placebo using RevMan 5.

How did you learn about the YODA Project?: 
Software Used: 
I am not analyzing participant-level data / I will not be using these software for analyses in the secure platform
Please clarify:: 
We need frequency data from all patients who particapated in the studies, maybe in form of CSRs, that we can analyse the data with RevManager in our office. The analysis can not be conducted using R, Stata, Python, or Open Office.
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><li><a href="/node/3774">NCT00924469 - COU-AA-201-DFCI - A Phase 2 Open-Label, Randomized, Multi-center Study of Neoadjuvant Abiraterone Acetate (CB7630) Plus Leuprolide Acetate and Prednisone Versus Leuprolide Acetate Alone in Men With Localized High Risk Prostate Cancer</a></li><li><a href="/node/3775">NCT01088529 - COU-AA-203 - A Randomized, Open-Label, Neoadjuvant Prostate Cancer Trial of Abiraterone Acetate Plus LHRHa Versus LHRHa Alone</a></li><li><a href="/node/3776">NCT01424930 - 212082PCR2008 - An Open-Label Study to Determine the Short-Term Safety of Continuous Dosing of Abiraterone Acetate and Prednisone in Modified Fasting and Fed States to Subjects With Metastatic Castration-Resistant Prostate Cancer</a></li><li><a href="/node/3847">NCT01314118 - 212082PCR2005 - A Multicenter, Open-label, Single-arm, Phase 2 Study of Abiraterone Acetate Plus Prednisone in Subjects With Advanced Prostate Cancer Without Radiographic Evidence of Metastatic Disease</a></li><li><a href="/node/3863">NCT01695135 - ABI-PRO-3001 - A Phase 3, Randomized, Double-blind, Placebo-Controlled Study of Abiraterone Acetate (JNJ-212082) Plus Prednisone in Patients With Metastatic Castration-Resistant Prostate Cancer Who Have Failed Docetaxel-Based Chemotherapy</a></li><li><a href="/node/3984">NCT02236637 - 212082PCR4001 - A Prospective Registry of Patients With a Confirmed Diagnosis of Adenocarcinoma of the Prostate Presenting With Metastatic Castrate-Resistant Prostate Cancer</a></li><li><a href="/node/4037">NCT00473512 - COU-AA-001 - A Phase I/II Open Label Study of the 17α-Hydroxylase/ C17,20 Lyase Inhibitor, Abiraterone Acetate in Patients With Prostate Cancer Who Have Failed Hormone Therapy</a></li><li><a href="/node/4038">NCT00485303 - COU-AA-004 - A Phase II Open Label Study of CB7630 (Abiraterone Acetate) and Prednisone in Patients With Advanced Prostate Cancer Who Have Failed Androgen Deprivation and Docetaxel-Based Chemotherapy</a></li><li><a href="/node/4039">NCT01685983 - 212082PCR2007 - A Phase 2 Open Label Study of Abiraterone Acetate (JNJ-212082) and Prednisolone in Patients With Advanced Prostate Cancer Who Have Failed Androgen Deprivation and Docetaxel-Based Chemotherapy. </a></li><li><a href="/node/4040">NCT00474383 - COU-AA-003 - A Phase II Open Label Study of CB7630 (Abiraterone Acetate) in Patients With Advanced Prostate Cancer Who Have Failed Androgen Deprivation and Docetaxel-Based Chemotherapy</a></li><li><a href="/node/4041">NCT00473746 - COU-AA-002 - Phase I/II Open Label Dose Escalation Study of the 17α-Hydroxylase/ C17,20-Lyase Inhibitor, Abiraterone Acetate in Hormone Refractory Prostate Cancer</a></li><li><a href="/node/4042">NCT01795703 - JNJ-212082-JPN-202 - A Phase II Study of JNJ-212082 (Abiraterone Acetate) in Metastatic Castration-Resistant Prostate Cancer Patients Who Have Received Docetaxel-based Chemotherapy</a></li><li><a href="/node/4043">NCT00544440 - COU-AA-BMA - An Observational Study of Continuous Oral Dosing of an Irreversible CYP17 Inhibitor, Abiraterone Acetate (CB7630), in Castration-Resistant Prostate Cancer Patients Evaluating Androgens and Steroid Metabolites in Bone Marrow Plasma</a></li><li><a href="/node/4044">NCT01867710 - 212082PCR2023 - A Randomized Phase 2 Study Evaluating Abiraterone Acetate With Different Steroid Regimens for Preventing Symptoms Associated With Mineralocorticoid Excess in Asymptomatic, Chemotherapy-naïve and Metastatic Castration-resistant Prostate Cancer (mCRPC) Patients</a></li></ol>
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