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2017-2326

Project Title: 
Development of a Global Outcome Measure for Rheumatoid Arthritis Clinical Trials
Specific Aims of the Project: 

Aim 1. To generate a list of descriptions for the outcomes associated with medications commonly used to treat RA. This effort will be led by core patient partners (co-investigators) on this grant. We will use an iterative process to develop descriptions for outcomes including both benefits and AEs as experienced by RA patients.

Aim 2. To generate equivalence classes of global outcomes using trajectory mapping (TM). While it is clear that the G-PROM will range from a ranking representing the “maximum possible benefit and no experienced toxicity” to “no benefit and death or life threatening toxicity”, intermediate levels require empirical data. We will use TM to determine how combinations of varying levels of benefits and AEs (between the highest and lowest desirability of overall outcome anchors) should be ranked.

Aim 3. To obtain preliminary estimates of the validity of the G-PROM as an instrument for measuring global outcomes in comparative RCTs for RA. We will obtain estimates for criterion validity using the raw data from previously published RCTs(5-6).

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

Application Status

Ongoing
Scientific Abstract: 

Background: Currently available outcome measures for patients with inflammatory arthritis do not provide patients with the information they need in order to make informed decisions. The results of randomized controlled trials are currently reported as either average improvement scores across study subjects, (e.g. DAS) or the percentage of patients attaining a specified amount of improvement (e.g., ACR 20, 50 and 70). The numbers of subjects experiencing specific adverse events (AEs) are reported separately. While based on sound scientific methods, this approach does not provide any information on the overall effect of treatment.
Objective: The aim of this project is to develop and assess the validity of a Global Patient-Reported Outcome Measure (G-PROM) to better quantify and compare the distribution of patients’ experiences on medications. The measure will combine the range of possible benefits and the full spectrum of harms of treatment at the individual patient level. The result will be a ranking of all trial subjects by the desirability of their overall outcome.
Design: We will obtain estimates for criterion validity using the raw data from three previously published RCTs.
Participants: Individuals with RA participating randomized controlled drug trials for RA.
Main outcome: A scale measuring desirability of patients’ overall outcome on RA medications that captures the total patient experience on medications.
Analysis: We will fit a separate ordinal regression models for each outcome of interest applying longitudinal and survival techniques

Brief Project Background and Statement of Project Significance: 

Best practices for patients with rheumatoid arthritis (RA)(1) (and possibly psoriatic arthritis(2-4)), call for patients to be treated-to-target (TTT). Adherence to this strategy requires ongoing disease activity monitoring and adjustments in treatment plans (i.e., changes or addition of medications) to achieve and maintain low disease activity or remission. TTT strategies are in large part possible because of the numerous treatment options currently available for patients with inflammatory arthritis. An RA patient failing methotrexate (MTX) monotherapy now has numerous treatment options to choose from.
Currently available outcome measures for arthritis, however, do not provide patients with the information they need to make an informed choice about their treatment options. Specifically, the results of randomized controlled trials (RCTs) are currently reported as either average improvement scores or the percentage of patients attaining a defined response. The numbers of subjects experiencing specific adverse events (AEs) are reported separately. While based on sound scientific methods, this approach does not quantify what is most important to patients: their overall experience on treatment. In the words of a patient with RA: “Patients have no way to determine the potential net benefit for a given treatment, much less to compare across treatments. What patients want to know is: What are my odds of getting better while enduring the lowest possible level of side effects for each medication? How will I feel overall on medication A compared to medication B?” Simultaneously weighing the efficacy and AEs of multiple drugs is also challenging for physicians, and makes it difficult for them to effectively engage their patients in shared decision-making. Thus, there is a need for more informative benefit: risk evaluation measures in rheumatology.

A scale measuring desirability of patients’ overall outcome will generate more informative comparative benefit:risk data than current approaches. The G-PROM will 1) improve evidence-based, patient-centered, decision making, 2) allow investigators to conduct meaningful comparative studies, and 3) allow the FDA to evaluate medications without over-reliance on the judgment of expert panels to determine whether the benefits of proposed new medications outweigh their harms. The methods used will follow the ACR criteria guidelines to ensure eligibility of the final product for endorsement.

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

The data requested in these proposal pertain to Aim 3, assessing validity of the G-PROM developed in Aim 1 and Aim 2.
We will use data collected on approximately 1800 RA patients from three different multicenter, double-blind randomized controlled trials using different classes of drugs for which we have access to the raw patient-level data.
Data source:
1. 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 (NCT00264550)
2. Rheumatoid Arthritis: Comparison of active therapy in patients with active disease despite methotrexate therapy (RACAT)
3. The PREMIER study: A multicenter, randomized, double-blind clinical trial of combination therapy with adalimumab plus methotrexate versus methotrexate alone or adalimumab alone in patients with early, aggressive rheumatoid arthritis who had not had previous methotrexate treatment

Inclusions/Exclusion Criteria:

The sample for this study will include adults ≥ 18 years with active RA who participated in one of the RCTs listed about (see Data Source).

Narrative Summary: 

This project aims to develop a new outcome measure that encompasses both benefits and harms of treatment at the individual patient level. The result will be a ranking of all trial subjects by the desirability of their overall outcome from “Remission without AEs” to “No clinical improvement and a life threatening AE (or death)”. Between these two extremes are mutually exclusive hierarchical levels of clinical outcomes ordered in terms of their desirability. Using this global outcome measure, randomized controlled trials could then report the percentage of patients classified into each level; improving patients’ understanding of the likelihood of the total effects of treatment on their lives.

Project Timeline: 

Project started / searches conducted: July 2017
Aim 1: Generate a list of descriptions for the outcomes associated with medications commonly used to treat RA: July 2017 - December 2017
Aim 2: Generate equivalence classes of global outcomes using trajectory mapping:: Winter 2018
Aim 3: Obtain preliminary estimates of the validity of the G-PROM as an instrument for measuring global outcomes in comparative RCTs for RA:
Data extraction / data request: Fall 2017- Winter 2018
Analysis and report writing: Summer/fall 2018 (dependent on data requests)
Aim to submit manuscript – end of 2018/first half of 2019.

Dissemination Plan: 

Publication in Arthritis Care and Research or other relevant peer-review journal
Presentation at American College of Rheumatology

Bibliography: 

1. Singh JA, Saag KG, Bridges SL, et al. 2015 American College of Rheumatology Guideline for the treatment of rheumatoid arthritis. Arthritis Care Res 2016;68:1-25.

2. Coates LC, Helliwell PS. Treating to target in psoriatic arthritis: How to implement in clinical practice. Ann Rheum Dis 2015.

3. Coates LC, Moverley AR, McParland L, et al. Effect of tight control of inflammation in early psoriatic arthritis (TICOPA): A UK multicentre, open-label, randomised controlled trial. Lancet 2015;386:2489-98.

4. Gladman DD. Is it time for treat to target in psoriatic arthritis? Lancet;386:2450-2.

5. O'Dell JR, Mikuls TR, Taylor TH, et al. Therapies for Active rheumatoid arthritis after Methotrexate failure. N Engl J Med 2013;369:307-18.

6. Keystone EC., et al. "Golimumab, a human antibody to TNF-α given by monthly subcutaneous injections, in active rheumatoid arthritis despite methotrexate: The GO-FORWARD Study" Annals of the rheumatic diseases 68(2009):789-796.

7. Evans SR, Rubin D, Follmann D, et al. Desirability of outcome ranking (DOOR) and response adjusted for duration of antibiotic risk (RADAR). Clin Infect Dis 2015;61:800-6.

What is the purpose of the analysis being proposed? Please select all that apply.: 
Other
Please Explain: 
New Research question to develop an improved scale for measuring patients' outcomes on different RA treatments
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

Global Patient-Reported Outcome Measure. G-PROM class levels will be generate based on the following measures in the existing data at 24 weeks:
1. Adverse events (list all)
2. Disease Activity Score (DAS28)
3. American College of Radiology (ACR) responses: ACR20 ACR50 ACR70

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

• Treatment groups (MTX + placebo, golimumab, 100mg, golimumab, 50mg + MTX, golimumab, 100mg + MTX)
• Duration of RA at baseline (years)
• Use of Disease modifying anti-rheumatic drugs (DMARDS) at baseline (yes/no)
• DAS28 score at baseline
• Duration of treatment adherence (weeks)

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

The following measures are requested for each participant at baseline:
• Age (years)
• Sex (male/female)
• Race (Caucasian, African American, other)
• Ethnicity (Hispanic/non-Hispanic)
• Body Mass Index (continuous)
• Current smoker (yes/no)
• Positive for rheumatoid factor (yes/no)
• Time since RA diagnosis (years)
• Use of NSAIDS (yes/no, dose)
• Use of oral corticosteroids (yes/no, dose)
Duration of previous MTX use (years)
Methotrexate dose, mg/week
The following measures are requested for each participant at baseline and each follow-up point
• American College of Radiology (ACR) responses
• DAS28 score
• Adverse events (list all per patient)
• Patient global assessment of pain (0−10 cm, VAS)
• Patient global assessment of disease activity (0−10 cm, VAS)
• Physician global assessment of disease activity (0−10 cm, VAS)
• Health Assessment Questionnaire Disability Index (HAQ-DI)
• C-reactive protein concentration (mg/L)
• Sharp/van der Heijde Score (SHS)

Statistical Analysis Plan: 

The data requested in these proposal pertain to Aim 3, assessing validity of the G-PROM currently being developed in Aim 1 and Aim 2 . Briefly, Aim 1 is a qualitative aim, involving the research team, rheumatologists recognized as experts in RA and an independent panel of 10 patients in an iterative process to generate and refine a list of outcomes based on patient-reported outcome measures already accepted by the FDA. For this project, we will include five benefit categories: Remission, ACR 70, ACR 50, ACR 20, and no improvement. Aim 2 will recruit approximately 400 participants with RA in a 2-stage process to complete an online survey to generate equivalency classes from the outcomes generated in Aim 1. We will examine five levels of benefit and an estimated 20 AEs (the exact number will be determined by Aim 1). Because of the exceedingly large sample size required to analyze these data in a single survey, we will perform the trajectory mapping procedure in two phases. In Phase 1, we will apply the TM technique to evaluate the overall structure of the AEs. This ranking will then be combined with the ranking over levels of benefit in order to create a partially-ordered hierarchy of side effect/benefit combinations. This is possible because levels of benefit [Remission, ACR 70, ACR 50, ACR 20 and no significant improvement (< ACR 20)] are already ordered. In Phase 2, we will confirm that subjects are indifferent between profiles in the same equivalence class (or level), but have strong preferences for the better profiles across equivalence classes.

The data requested in this proposal will be used to address Aim 3 of the study described above (see Specific Aims and Research Methods). We will combine de-identified individual patient-level data on participants from each of the trials. The combined dataset will be stored on a secure data sharing platform. Using data collected on AEs and benefits in these trials, we will classify each patient using the G-PROM and generate a ranking of all trial subjects by their overall outcome score. We will then calculate the probability of a better ranking for a randomly selected subject from the intervention compared to the control arm. This probability is calculated by the number of between-treatment comparisons in which a subject has a higher score in the intervention compared to a subject in the control arm divided by total number of possible pairwise comparisons(7). If there is no difference in the distribution of the scores, the probability is close to 50% (95% CI). We will then compare conclusions generated using the G-PROM to those reported in the original trial(5-6).
To assess the concurrent validity of the G-PROM, we will examine associations between G-PROM rankings and disease activity measures at 14 weeks. We will also examine the association between duration of RA and number of disease modifying anti-rheumatic drugs (DMARDs) at baseline and G-PROM rankings. We expect that longer duration of disease and greater number of DMARDS at baseline will be associated with worse G-PROM ranking. We will assess predictive validity by comparing baseline and 24-week G-PROM rankings to disease at 24-weeks. Additionally, we will assess predictive validity by examining the association between G-PROM scores and the total number of weeks a participant remains on their assigned treatment (an indicator of symptom improvement and tolerance of side effects). We will fit a separate ordinal regression models for each outcome of interest applying longitudinal and survival techniques when appropriate. Significant associations (p<0.05) between G-PROM and each of these measures will be considered evidence for concurrent and predictive criterion validity. All analyses will be conducted using SAS software available on the secured data storing platform.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><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></ol>
Make Publicly Available : 
Year of Data Access: 
2018

2017-2191

Project Title: 
Identifying heterogeneous treatment effects from canagliflozin: development and validation of models for HbA1c reduction and adverse event risk
Specific Aims of the Project: 

Study Objective:
To develop and validate predictive models for individualized estimation of canagliflozin HTEs on each of two outcome measures: absolute percent reduction in hemoglobin A1c, and absolute risk increase for a serious adverse event.

Specific hypothesis to be tested:
Pre-randomization participant characteristics chosen based on prior theory (specific demographics, vital signs, laboratory biomarkers, and baseline medication use) can separate participants who experience lower from higher absolute percentage point reduction in hemoglobin A1c (%), and participants who experience lower from higher absolute risk increase in serious adverse events when taking canagliflozin.

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: 

Background: Identifying heterogeneous treatment effects (HTEs) is important for the treatment of type 2 diabetes, particularly because medications chosen after metformin monotherapy pose both potential benefits (e.g., hemoglobin A1c reduction, cardiovascular disease risk reduction), and potentially serious risks (adverse events such as urogenital infections).
Objective: To identify HTEs from canagliflozin.
Study design: Development and validation of risk models for reduction in HbA1c, reduction in atherosclerotic cardiovascular disease, and increase in probability of serious adverse events.
Participants: N = 5,971 from eight randomized, double-blind canagliflozin trials in YODA.
Main outcome measures: Absolute percentage point decrease in HbA1c at 52 weeks; and absolute probability of serious adverse event at 52 weeks.
Statistical analysis: A limited subset of theory-based potential predictor variables for HTEs have been chosen for potential inclusion in the models. Gradient forest analysis will be performed using the pre-randomization values of these potential predictors. Gradient forest analysis develops multivariate models for HTEs in each outcome measure, based on repeated cross-validation of decision trees that are constructed to explain variation in observed treatment effect (absolute percentage reduction in HbA1c, absolute risk increase in serious adverse event rate) between study arms among patient subgroups. A 75% stratified random sample across all trials will be used for derivation and internal cross-validation, with the remaining

Brief Project Background and Statement of Project Significance: 

A goal of precision medicine is to identify patients more likely to experience benefit or harm from a given therapy (heterogeneous treatment effects, HTEs). HTEs are difficult to identify through typical univariate subgroup analyses, which have limited statistical power (1–3). Additionally, clinical care is not well-informed by univariate analyses (e.g., if males experience benefit but older people experience harm, how should a practitioner counsel an older male?). Consequently, multivariate HTE modeling has been recommended to advance personalized decision-making (4–6), but poses the risk of generating false positive results with multiple testing.
Recently, machine learning methods—particularly gradient forest analysis (7)—have aided identification of HTEs. Gradient forest analysis can separate trial populations into subgroups characterized by multiple simultaneous characteristics, using cross-validation and P-value correction to reduce false positives (7,8). We have adapted the gradient forest method to help identify HTEs when pooling data across trials with different study designs, including trials with differing medication dosage, co-occurring medications, or control groups, using principles from network meta-analysis (9–11) (NMA). The technique can create new risk prediction tools from individual participant data, while accounting for diversity between studies. This application will be the first use of the technique, to our knowledge, to clinical data; we have applied the method to non-network meta-analysis setting from single trials, but only simulated pooled analyses to establish non-bias and low false-positive rates (8).
Estimating HTEs for new glycemic agents for type 2 diabetes is particularly important, as individualizing glycemic treatment is now recommended (12), but how best to individualize treatment remains unclear. Prior NMAs suggest that newer diabetes drugs present large potential benefits and large potential risks (13,14). Canagliflozin, a sodium glucose co-transporter 2 (SGLT-2) inhibitor, increases glucose excretion in urine, significantly reducing HbA1c and associated disease complications (15,16). But canagliflozin also presents increased risk of adverse events including urogenital infections, bony fractures, and lower limb amputations (15,17). Those receiving the most benefit from canagliflozin in terms of reduced HbA1c were not those experiencing serious adverse events in published trials to date—suggesting that HTE models may be clinically helpful to distinguish high-benefit from high-risk patients (15). Canaglifozin had greater A1c reduction than almost any other new diabetes medicines. In spite of that, the risk of limb amputation in particular may make it too high-risk for clinical use. Therefore, identifying which populations are lower versus higher benefit and lower versus higher risk is of clinical importance.  
Hence, our development of HTE models in this study may advance scientific knowledge about the development of benefit/risk models to personalize medical therapies. The study may also add to generalizable knowledge for treatment of type 2 diabetes.

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

All participants in YODA’s randomized, double-blind trials including canagliflozin, with at least 52 weeks follow-up, will be included. We anticipate N = 5,971 participants with type 2 diabetes, at least 18 years of age, comparing canagliflozin at any dosage to placebo or other diabetic agents, with co-administration of other diabetic agents in both the intervention and control group.

Narrative Summary: 

In this study, we seek to develop and validate risk models for estimating: (i) decrease in hemoglobin A1c (HbA1c), and (ii) increase in serious adverse event risk from canagliflozin, using individual participant data from randomized controlled trials. Multivariate risk models have the potential to identify subgroups of patients that have a greater probability of benefit or of harm from a given therapy (heterogeneous treatment effects, HTEs). Here, we plan to identify HTEs through methods that aim to reduce the chance of false-positive associations, and produce unbiased effect estimates when a medication has been compared at varying dosages with different co-occurring medications.

Project Timeline: 

Anticipated project start date: November 1, 2017
Analysis completion date: February 31, 2017
Date manuscript drafted: April 31, 2017
First submission for publication: June 31, 2017
Date results reported back to YODA: June 31, 2017

Dissemination Plan: 

Anticipated products: Peer-reviewed journal publication
Target audience: primary care, internal medicine, and endocrinology colleagues
Potentially suitable journal for submission: The Lancet Diabetes & Endocrinology

Bibliography: 

1. VanderWeele TJ, Knol MJ. Interpretation of subgroup analyses in randomized trials: heterogeneity versus secondary interventions. Ann Intern Med. 2011 May 17;154(10):680–3.
2. Wallach JD, Sullivan PG, Trepanowski JF, Sainani KL, Steyerberg EW, Ioannidis JPA. Evaluation of Evidence of Statistical Support and Corroboration of Subgroup Claims in Randomized Clinical Trials. JAMA Intern Med [Internet]. 2017 Feb 13 [cited 2017 Feb 21]; Available from: http://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2601419
3. Basu S, Sussman JB, Hayward RA. Detecting Heterogeneous Treatment Effects to Guide Personalized Blood Pressure Treatment: A Modeling Study of Randomized Clinical Trials. Ann Intern Med. 2017 Jan 3;154(10):680–3.
4. Burke JF, Hayward RA, Nelson JP, Kent DM. Using Internally Developed Risk Models to Assess Heterogeneity in Treatment Effects in Clinical Trials. Circ Cardiovasc Qual Outcomes. 2014 Jan 1;CIRCOUTCOMES.113.000497.
5. Kent DM, Rothwell PM, Ioannidis JP, Altman DG, Hayward RA. Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal. Trials. 2010 Aug 12;11:85.
6. Hayward RA, Kent DM, Vijan S, Hofer TP. Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis. BMC Med Res Methodol. 2006 Apr 13;6:18.
7. Athey S, Imbens G. Recursive partitioning for heterogeneous causal effects. Proc Natl Acad Sci. 2016 Jul 5;113(27):7353–60.
8. Baum A, Scarpa J, Bruzelius E, Tamler R, Basu S, Faghmous J. Targeting weight loss interventions to reduce cardiovascular complications of type 2 diabetes: a machine learning-based analysis of heterogeneous treatment effects in The Look AHEAD Trial. Lancet Diabetes Endocrinol. 2017;epub ahead of print.
9. Cleophas TJ, Zwinderman AH. Network Meta-analysis. In: Modern Meta-Analysis [Internet]. Springer; 2017 [cited 2017 Sep 1]. p. 145–155. Available from: http://link.springer.com/chapter/10.1007/978-3-319-55895-0_12
10. Dagne GA, Brown CH, Howe G, Kellam SG, Liu L. Testing moderation in network meta-analysis with individual participant data. Stat Med. 2016 Jul 10;35(15):2485–502.
11. Dias S, Sutton AJ, Ades AE, Welton NJ. Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Med Decis Making. 2013;33(5):607–617.
12. American Diabetes Association. 6. Glycemic Targets. Diabetes Care. 2017 Jan 1;40(Supplement 1):S48–56.
13. Palmer SC, Mavridis D, Nicolucci A, Johnson DW, Tonelli M, Craig JC, et al. Comparison of clinical outcomes and adverse events associated with glucose-lowering drugs in patients with type 2 diabetes: a meta-analysis. Jama. 2016;316(3):313–324.
14. Shehab N, Lovegrove MC, Geller AI, Rose KO, Weidle NJ, Budnitz DS. US Emergency Department Visits for Outpatient Adverse Drug Events, 2013-2014. JAMA. 2016 Nov 22;316(20):2115–25.
15. Neal B, Perkovic V, Mahaffey KW, de Zeeuw D, Fulcher G, Erondu N, et al. Canagliflozin and Cardiovascular and Renal Events in Type 2 Diabetes. N Engl J Med. 2017 17;377(7):644–57.
16. Cefalu WT, Leiter LA, Yoon K-H, Arias P, Niskanen L, Xie J, et al. Efficacy and safety of canagliflozin versus glimepiride in patients with type 2 diabetes inadequately controlled with metformin (CANTATA-SU): 52 week results from a randomised, double-blind, phase 3 non-inferiority trial. The Lancet. 2013 Sep 14;382(9896):941–50.
17. Fadini GP, Avogaro A. SGTL2 inhibitors and amputations in the US FDA Adverse Event Reporting System. Lancet Diabetes Endocrinol [Internet]. 2017 [cited 2017 Sep 1]; Available from: http://www.thelancet.com/journals/landia/article/PIIS2213-8587(17)30257-7/abstract
18. Storey JD, Taylor JE, Siegmund D. Strong control, conservative point estimation, and simultaneous conservative consistency of false discovery rates: A unified approach. J R Stat Soc Ser B. 2004;66:187–205.
19. Wager S, Athey S. Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc [Internet]. 2017 [cited 2017 Jun 22];(just-accepted). Available from: http://www.tandfonline.com/doi/abs/10.1080/01621459.2017.1319839

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
New research question to examine treatment safety
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

Absolute percentage point reduction in hemoglobin A1c (%), defined as a continuous measure with exact hemoglobin A1c reduction in each canagliflozin treatment arm versus control arm between week 0 and week 52.
Absolute risk increase in each of two serious adverse events (two separate outcomes of urogenital infection, and lower limb amputation), defined as probability of the serious adverse event by week 52 in each canagliflozin treatment arm versus control arm.

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

Randomization to canagliflozin treatment group (dummy variable 1/0).

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

Age (in years), Sex (male/female), Race/ethnicity (White/Black/Other), baseline systolic and diastolic blood pressure (mmHg), baseline fasting lipids (total, HDL, LDL and triglycerides in mg/dL), baseline body mass index (kg/m^2), baseline estimated glomerular filtration rate by MDRD equation (mL/min/1.73m^2), baseline hemoglobin A1c (%), baseline fasting plasma glucose (mg/dL), prior history of neuropathy or diabetic ulcer, prior history of urogenital infection.

Statistical Analysis Plan: 

Descriptive analysis will include summary statistics of the above variables of interest by treatment arm within and across all trials
Multivariate non-parametric analysis will involve gradient forest analysis, which proceeds in four steps. First, 75% of the pooled individual participant data across all included trials will be divided in half randomly, with an equal number of canagliflozin and control arm participants in each of the two data subsets (the remaining 25% of the data will be held out for interval validation). Second, variables from the class of predictor variables of interest will be chosen by randomly sampling subsets of potential predictors for HTEs (listed above), to construct a decision-tree made of those predictors that could split the first of the two subsamples of data into subgroups with higher and lower treatment effect. Treatment effect is defined as the absolute difference in hemoglobin A1c, ASCVD or serious adverse event probability between the canagliflozin and control group arms, with effect modifiers included for the individual study, canagliflozin dosage, co-occurring medications, and whether the control arm is an active treatment (glimepiride or sitagliptin) rather than placebo (9–11). Subgroups are required to be >5% of the overall pooled study sample. Third, once the initial decision tree is constructed from the first subsample of data, the values of each predictor that define branches in the decision tree are refined using the second subsample of data, so that the final subgroups at the bottom of the tree (“leaves” of the tree) have maximum between-group differences and minimum within-group differences in treatment effect. Refinement in the second data subset reduces the influence of outliers, and helps produce unbiased HTE estimates (7). The overall approach is repeated 4,000 times from the first step, to produce a “forest” of trees by repeated random resampling of the data (cross-validation). No change in estimated variable importance is typically observed beyond 4,000 trees (7), but this will be empirically assessed to determine if a higher number of trees is necessary. Variable importance is defined as the frequency with which a given variable was incorporated into a tree at the first, second, and further split points (i.e., a variable can change positions between trees, but variable selection for each position is tracked to monitor its importance). The significance of the interaction term between subgroup and therapy arm will be tested using the q-value correction approach, which will correct to a P<0.05 threshold for the empirical probability of obtaining false-positive HTE when performing multiple tests (18); subgroups with significance by the q-value threshold will be maintained. After the forest is constructed and cross-validated, the summary (average) decision tree that placed those variables of highest importance at each split point among the forest of trees will be identified.
To assess performance of the summary decision tree, absolute risk difference in the probability of each outcome will be calculated between the canagliflozin and control arms within each subgroup (leaf) of the trial population, and across the subgroups (nonparametric Jonckherre test for trend across subgroups). Although there are no formal power analyses for causal forest procedures, prior simulations suggest that at least 10 events per predictor variable should be observed in the pooled control arms (>130 events) (19); there were over three times as many events for each of the two severe adverse event outcomes among the included trial participants.
In sensitivity analyses, the decision tree will be reconstructed using just the subset of trials in which canagliflozin was compared to placebo (6 of the 8 trials, N = 3,672), and to separately analyze persons with canagliflozin 100mg and with 300mg to identify effectiveness of the effect modifier terms.

How did you learn about the YODA Project?: 
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></ol>
Make Publicly Available : 

2017-2036

Project Title: 
Determine the growth and regression rate constant and the fractional cell kill of abiraterone acetate in prostate cancer
Specific Aims of the Project: 

AIM 1: Harvest data and estimate the growth and regression rates and the fractional cell kill of prostate cancer treated with abiraterone acetate.
Aim 1.1: Harvest data from patients enrolled in NCT00638690 and NCT00887198 to estimate the growth and regression rates as well as the fractional cell kill while receiving therapy.
AIM 2: Assess the efficacy of abiraterone acetate as a prostate cancer therapy by establishing correlations between the rate of growth and the overall survival.
Aim 2.1: Conduct statistical comparisons of the abiraterone acetate data to data previously evaluated by the investigators from (1) other publicly available databases and (2) Veterans treated within the Veterans Health Administration system.

Note: The efficacy of abiraterone is already established. It is approved by the FDA. This will establish a correlation between the growth rate constant on abiraterone and overall survival (OS), an important finding given the well-established value of OS as the FDA "gold standard". That we will establish this we have no doubt. So what we will do is estimate the growth rate constants in all of these patients and this will be a measure in that patient of a value that correlates with OS and this of course is a measure of efficacy

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: When an oncologist treats a patient with cancer the fraction of cancer sensitive to the therapy regresses while simultaneously the fraction resistant to therapy grows, both at a constant rate. The quantity of tumor may be larger or smaller than at the start, depending on which of the two simultaneous phenomena dominates. Using a novel yet extensively tested method of analysis we can discern these two simultaneous processes and establish for each tumor its rate of growth and regression during treatment. Treatment efficacy is the net result of two simultaneous phenomena, correlate exceptionally well with overall survival - the FDA gold standard for efficacy.
Objective: Examine outcomes in patients with prostate cancer treated with abiraterone.
Study Design: Retrospective analysis of NCT00638690 and NCT00887198 data.
Participants: Patients with prostate cancer.
Main Outcome Measure(s): The mean and medians of growth rate constant, regression rate constant and fractional cell kill are estimated and these are in turn utilized for statistical analysis.
Statistical Analysis:
• Comparisons of growth rate distribution: Wilcoxon two-sided/ Kruskal Wallis tests.
• OS probabilities: Kaplan-Meier method.
• Landmark survival analysis of OS: landmark and Cox model.

Brief Project Background and Statement of Project Significance: 

Background:
Prostate cancer (PC) is the second most frequently diagnosed cancer and second leading cause of cancer death in males in the US and Europe. For patients with locally advanced PC or those who develop metastatic disease, androgen deprivation therapy (ADT) or surgical castration has been the mainstay of treatment given the importance of the androgen receptor (AR) in development and progression of PC. However, despite induction of biochemical and clinical response by ADT in >90% of treated patients, progression to castration-resistant prostate cancer (CRPC), defined as progression despite low testosterone levels, occurs after a median of 24–36 months. And while we generally think PC is an indolent disease, median survival times of patients with metastatic CRPC (mCRPC) are only 9.1- 21.7 months without treatment.
The recognition that CRPC retained androgen responsiveness and that interfering with androgen signaling could effect tumor responses in CRPC has fueled a revolution in treatment –emphasizing the therapies aimed at targeting the AR. The approval of abiraterone and then of enzalutamide provided novel, tolerable and effective options to target the AR. Additionally in the past two years, three RCTs evaluated the early addition of docetaxel to ADT in “hormone-sensitive metastatic prostate cancer” and the results have been both surprising and gratifying. So that a recent meta-analysis concluded the data “clearly shows a significant impact on OS with the concomitant administration of docetaxel and ADT in patients with metastatic hormone-sensitive prostate cancer”.
Project Significance. The increasing numbers of options for PC present therapeutic challenges. Because none is curative, tolerability and efficacy influence decisions. Our approach allows one to estimate and update a tumor’s growth rate with each PSA result. This growth rate can be compared against values in other patients receiving the same or different therapies including those enrolled in pivotal clinical trials; or Veterans that have received the same therapy or even a subset – for example, African American men older than 70 years of age. It allows one to make optimal choices by leveraging “big data” to inform decisions on individual patients.
We have explored tumor burden in mCRPC using data from several studies including single and multi-institutional data sets. We have explored tumor burden in mCRPC using data from several studies including single and multi-institutional data sets. We are now poised to explore additional patient data, including data hosted on YODA. This data will allow us to benchmark the efficacy of abiraterone in Veterans using data from the VHA records and in turn, allow us to compare the efficacy of both abiraterone and enzalutamide in this very diverse patient population.
No elements other than PSA values and the dates when they were obtained are needed. The added value is that we are confident we will establish a correlation between the growth rate constant and overall survival the FDA "gold standard". This then emerges as a very valuable finding as regards clinical trials and also the administration of this drug class in patients.

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

We developed an R package, designated tumgr, AVAILABLE at R-Studio that estimates tumor growth and regression rates. Four equations are evaluated and the one that minimizes the Akaike Information Criterion (AIC) is selected as the best fit. Experience with nearly 20,000 individual patient data has shown that, on average, 90-95% of data in a trial can be fit to the equations. Only data not described by one of the four equations is not analyzed. We will focus on the rate of growth, but also examine and understand the meaning / value of simultaneously occurring regression rate.
Growth rates can be estimated from PSA data or tumor measurements; we have used both. We recognize available tumor measurements are of only some lesions. We do not calculate "PSA doubling times" since they cannot be estimated when tumor quantity is shrinking and then growing as occurs with abiraterone, but only after the nadir is reached and "clinically" only growth is occurring; our equations include both a regression and growth rate constants.;We have conducted and published analyses using imaging measurements, PSA values, calcitonin values (in medullary thyroid cancer), and M spikes (in multiple myeloma).

Narrative Summary: 

Prostate Cancer is the most common cancer in men, monitored by PSA, a serum marker accurately reflecting disease burden. Treatment efficacy is the net result of two simultaneous phenomena: regression of the sensitive tumor fraction and the growth of the resistant tumor fraction increasing at a fixed rate. Using a novel method of analysis we can discern these two simultaneous processes and establish for each tumor its rate of growth and regression during treatment. In this proposal we will determine efficacy of abiraterone by establishing correlations between the rate of growth and the overall survival, the FDA gold standard for efficacy.

Project Timeline: 

0-2 months: Analyze data
2-4 months: Assess efficacy and compare results to other therapies
4-6 months : Submit for publication

Dissemination Plan: 

Dissemination Plan:
Anticipated products: Manuscript within 6 months.
Target Audience: Medical Oncologists and Outcomes researchers
Expectation for study manuscript(s): highly statically valid comparison of data: novel data analysis in a very diverse group of patients.
Publications: High impact journal such as Lancet Oncology, JAMA and JAMA Oncology

Bibliography: 

1. Stein WD, Figg WD, Dahut W, et al. Tumor growth rates derived from data for patients in a clinical trial correlate strongly with patient survival: a novel strategy for evaluation of clinical trial data. Oncologist 2008a; 13:1046–1054.
2. Stein WD, Yang J, Bates SE, Fojo T. Bevacizumab reduces the growth rate constants of renal carcinomas: a novel algorithm suggests early discontinuation of bevacizumab resulted in a lack of survival advantage. Oncologist 2008b; 13:1055–1062.
3. Stein WD, Huang H, Menefee M et al. Other paradigms: growth rate constants and tumor burden determined using computed tomography data correlate strongly with the overall survival of patients with renal cell carcinoma. Cancer J. 2009; 15:441-7.
4. Stein WD, Gulley JL, Schlom J et al. Tumor regression and growth rates determined in five intramural NCI prostate cancer trials: the growth rate constant as an indicator of therapeutic efficacy. Clin Cancer Res 2011; 17:907–917.
5. Stein WD, Wilkerson J, Kim ST, et al. Analyzing the pivotal trial that compared sunitinib and IFN-alpha in renal cell carcinoma, using a method that assesses tumor regression and growth. Clin Cancer Res 2012; 18:2374–2381.
6. Wilkerson J. tumgr: Tumor Growth Rate Analysis. R package version 0.0.4. 2016. http://CRAN.R-project.org/package=tumgr (accessed Mar 25, 2016).
7. Wilkerson J, Abdallah K, Hugh-Jones C, Curt G, Rothenberg M, Simantov R, Murphy M, Morrell J, Beetsch J, Sargent DJ, Scher HI, Lebowitz P, Simon R, Stein WD, Bates SE, Fojo T. Estimation of tumour regression and growth rates during treatment in patients with advanced prostate cancer: a retrospective analysis. Lancet Oncol. 2017; 18:143-154.

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

• Growth rate constant
• Regression rate constant
• Fractional cell kill

These values are assigned both numerical and logarithmic numbers. For entire data sets, mean and medians with confidence intervals are estimated and these are in turn utilized for statistical analysis.

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

Growth rate constant, regression rate constant and fractional cell kill will be estimated for individual patients and for specific subgroups. In each subgroup, mean and medians with their confidence intervals will be used to assess statistical similarities or not. As the values represent a continuum and not predefined bins, there is no categorization. The only categorization is a descriptive one where each individual data set is defined according to whether one or more of three variables (growth rate constant, regression rate constant and fractional cell kill) comprise equation that best describes the fit of the data. All of these methodologies have been validated and are described in our previous publications.

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

Overall survival, progression free survival, age, racial/ethnic demographics, and if available Gleason scores.

Statistical Analysis Plan: 

Patient datasets with sufficient data will be analyzed using four formulae and indicated as either included (with selected model indicated) or excluded (non-significant predictors where no model converged indicated as ‘not fit’ or those with only 2 data points differing by <20%). Comparisons of growth rate distributions will be done by Wilcoxon two-sided tests (where groups analyzed = 2) or by Kruskal Wallis tests (where groups analyzed >2) followed by a Dunn’s test for pairwise difference if there is an overall difference. The Kaplan-Meier method will estimate OS probabilities. Landmark survival analysis of OS and a Cox regression will be performed with the log of g (estimated from data prior to landmark) as the single predictor using the R package survival to obtain a measure of concordance (C-index) between g and OS. Landmark will be chosen as a time point far enough after treatment initiation to allow for reliable estimation of g, but close enough to randomization so that a limited number of patients have died. Additionally, the incremental value of g will be evaluated by comparing a Cox model containing baseline variables (age, race, treatment) with a model containing baseline variables and g, to obtain the change in the C-index after the addition of g information using 1000 iterations of perturbation re-sampling via the R package survC1. By incremental g evaluation we are looking to define how much additional model accuracy (as assessed by the C-index) the addition of g to the model added.
As we note above, "Patient datasets with sufficient data will be analyzed and noted using four formulae and indicated as either included (with selected model indicated) or excluded (non-significant predictors where no model ...... by <20%)". A given data from one patient either can be fit to one of the equations with a p value of less than 0.1 or it cannot. If it cannot then it is not included in the analysis. With prostate cancer about 88-92% of data can be fit. Data that does "not fit" is often patients with very low numbers that "just bounce around" or those with only two values who then went off study. As for landmark analysis we can argue that since there is no established standard and quite frankly having done many of these, they add very little to an analysis. So basically we do as we did in our Lancet Oncology manuscript, we conduct multiple landmark analyses to satisfy everyone. No one can say for such a trial what MUST be the landmark analysis. So any value given is pulled out of the air. Basically as I said we do multiple landmark analyses and all corroborate each other.
Finally, because the formulae used will include time (t), the analysis is not affected by assessment intervals such that if the intervals of two studies are different or if scheduling difficulties require some intervals to be longer or shorter the estimates of phi, g and d, are not affected since these estimates are a global average over all data points for that patient. This in turn allows the data to be presented as one output. Note also that estimates of phi are determined not only by the falling part of the tumor size curve (PSA as surrogate for this) but also by data form the re-growing phase.
The first step will be to estimate the rates of growth and regression in the YODA data. If OS data is available we are CONFIDENT we will establish a correlation with OS. With that data in hand we can then use it as a "benchmark or reference" with any other data set. We have data on over 5000 VA patients treated with abiraterone and enzalutamide including the largest number of African American men ever studied. That data has been analyzed and it is validated, incredibly robust, correlates with OS, and has been analyzed across the entire VA system. With the YODA analysis finished we are literally then "just a few clicks away" from comparing the data in the YODA data set to our own data. We have done these comparisons extensively.

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

2017-2031

Project Title: 
Association between Patient-reported Outcomes and Endoscopic Healing in Ulcerative Colitis: A meta-analysis
Specific Aims of the Project: 

Study hypothesis:
Can patient-reported outcomes be used as a surrogate to monitor patients who achieved mucosal healing on biologic therapies?
Study objectives:
1. Evaluate the association of rectal bleeding score = 0 to predict mucosal healing (endoscopic subscore = 0 or 1).
2. Evaluate the association of stool frequency score = 0 to predict mucosal healing (endoscopic subscore = 0 or 1).
3. Evaluate the association of combined rectal bleeding and stool frequency score = 0 to predict mucosal healing (endoscopic subscore = 0 or 1)

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

Application Status

Complete
Scientific Abstract: 

Background: When treating patients with ulcerative colitis (UC), patient-reported symptoms of rectal bleeding and increased stool frequency are accompanied by endoscopic changes. The goal of treatment is to normalize symptoms and improve quality of life for patients. Many opinion leaders are now proposing targeting mucosal healing as a goal of treatment in UC. However, repeated endoscopic assessments are expensive so surrogates of mucosal healing are important to identify and use.
Objective: The purpose of this study is to conduct a meta-analysis of data from biologic studies in UC correlating patient-reported outcome scores (stool frequency, rectal bleeding) with endoscopic healing.
Study Design: Meta-analysis of clinical trial data for all approved biologics in UC (infliximab, adalimumab, golimumab, vedolizumab)
Participants: Moderate-Severe UC patients from clinical trial programs who were treated with biologic therapies
Main Outcome Measure: Pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of rectal bleeding score =0, stool frequency score=0, and combined rectal bleeding + stool frequency score = 0 to predict endoscopic healing.
Statistical analysis: Comprehensive meta-analysis software will be used. Individual study level data will be combined to determined pooled sensitivity, specificity, PPV, and NPV of all the independent variables of interest. Receiver operating characteristic curves will be drawn for each of the variables of interest to compare relative performance.

Brief Project Background and Statement of Project Significance: 

When treating patients with ulcerative colitis (UC), patient-reported symptoms of rectal bleeding and increased stool frequency are accompanied by endoscopic changes. The goal of treatment is to normalize symptoms, heal the mucosa, and improve quality of life for patients. In clinical trials involving patients with UC, clinical remission or response is commonly defined using the Mayo score which is a composite of patient reported outcomes (i.e. stool frequency and rectal bleeding subscores) and physician reported outcomes (ie, endoscopy subscore and physician’s global assessment [PGA]).
Recently, the STRIDE initiative proposed a composite remission target based on clinical and patient-reported outcomes (including resolution of rectal bleeding and diarrhea/altered bowel habits)and the absence of ulceration on endoscopy (either flexible sigmoidoscopy or colonoscopy). However, repeated endoscopic assessment for mucosal healing is expensive and may not be feasible at some centres or in certain countries. Biomarkers or surrogates of mucosal healing are important to identify in this case.
Studies of biologic therapies which have been approved for UC, including infliximab, golimumab, adalimumab, and vedolizumab, all include endoscopic assessments and Mayo scores including the patient-reported outcomes. A publication looking at the association of patient-reported outcomes with mucosal healing in adalimumab-treated patients reported that the positive predictive value of combined patient-reported outcomes of stool frequency and rectal bleeding scores equal to zero was reasonably high (90%) for complete mucosal healing (1). They also reported that the sensitivity of stool frequency alone is only 29% in patients with complete mucosal healing, so this symptom alone cannot predict mucosal healing (1).
The objective of this meta-analysis is to explore the association of patient-reported outcomes of stool frequency or rectal bleeding with endoscopic healing in patients treated with biologic therapies. This will help determine whether patient symptoms are useful enough to predict mucosal healing, or whether endoscopy or other biomarker tests need to be conducted to be reasonably confident this outcome has been achieved.

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

Data sources:
Post-hoc analyses of adalimumab, vedolizumab, and golimumab studies examining association of patient-reported outcomes and endoscopic healing have been published or presented in abstract form and have already been attained. We are seeking this same data from the infliximab in ulcerative colitis study (ACT2) in order to conduct this meta-analysis.
Inclusion:
All studies including UC patients treated with biologic therapies at which there is data available which correlate patient-reported outcomes with mucosal healing at a short-time interval from initiating of therapy (<= 12 weeks) and/or a long-time interval (> 12 weeks)
Exclusion:
Studies of patients with Crohn's disease
Studies of non-biologic therapies
Studies without endoscopic healing assessments

Narrative Summary: 

When treating patients with ulcerative colitis (UC), patient-reported symptoms of rectal bleeding and increased stool frequency are accompanied by endoscopic changes. The goal of treatment is to normalize symptoms and improve quality of life for patients. Many opinion leaders are now proposing targeting mucosal healing as a goal of treatment in UC. However, repeated endoscopic assessments are expensive so surrogates of mucosal healing are important to identify and use. The purpose of this study is to conduct a meta-analysis of data from biologic studies in UC correlating patient-reported outcomes (stool frequency, rectal bleeding) with endoscopic healing.

Project Timeline: 

Anticipated project start date: January 1, 2018
Anticipated analysis completion date: January 31, 2018
Date manuscript drafted and first submitted for publication: February 28, 2018
Date results reported back to the YODA project: May 31, 2018

Dissemination Plan: 

We plan to disseminate the results with a manuscript publication. We anticipate publication in a mid-tier gastroenterology journal such as Alimentary Pharmacology & Therapeutics or Journal of Crohn's and Colitis.

Bibliography: 

(1) Jharap et al. Randomised clinical study: discrepencies between patient-reported outcomes and endoscopic appearance in moderate to severe ulcerative colitis. Aliment Pharmacol Ther 2015; 42(9): 1082-92.

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

From each biologic study for UC, we will create a 2X2 table looking at the sensitivity, specificity, positive predictive value, and negative predictive value of the following:
1. rectal bleeding score = 0 vs rectal bleeding score >=1 compared to mucosal healing score = 0 vs. mucosal healing score >=1
2. rectal bleeding score = 0 vs rectal bleeding score >=1 compared to mucosal healing score = 0 or 1 vs. mucosal healing score >=2
3. stool frequency score = 0 vs stool frequency score >=1 compared to mucosal healing score = 0 vs. mucosal healing score >=1
4. stool frequency score = 0 vs stool frequency score >=1 compared to mucosal healing score = 0 or 1 vs. mucosal healing score >=2
5. rectal bleeding score = 0 and stool frequency score = 0 vs all other values for rectal bleeding/stool frequency compared to mucosal healing score = 0 vs. mucosal healing score >=1
6. rectal bleeding score = 0 and stool frequency score = 0 vs all other values for rectal bleeding/stool frequency compared to mucosal healing score = 0 or 1 vs. mucosal healing score >=2
We will use meta-analysis software to pool these numbers together and create receiver operating characteristic curves.

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

Predictor variables are the patient-reported outcomes, and they will be categorized as above.

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

For the purpose of this meta-analysis, we are only seeking data of patient-reported outcomes and their association with Mayo endoscopic subscore.

Statistical Analysis Plan: 

Meta-analysis software (Comprehensive meta-analysis) will be used to conduct this meta-analysis. Individual study level data will be combined to determined pooled sensitivity, specificity, positive predictive value, and negative predictive values of all the independent variables of interest. Receiver operating characteristic curves will be drawn for each of the variables of interest to compare their relative performance.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/156">NCT00036439 - C0168T37 - A Randomized, Placebo-controlled, Double-blind Trial to Evaluate the Safety and Efficacy of Infliximab in Patients With Active Ulcerative Colitis</a></li><li><a href="/node/157">NCT00096655 - C0168T46 - A Randomized, Placebo-controlled, Double-blind Trial to Evaluate the Safety and Efficacy of Infliximab in Patients With Active Ulcerative Colitis</a></li><li><a href="/node/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></ol>
Make Publicly Available : 
Year of Data Access: 
2017
Associated Data: 
Results

2017-1966

Project Title: 
Psychosis break through antipsychotic maintenance medication: An individual participant data meta-analysis
Specific Aims of the Project: 

Aim 1: To measure the risk of BAMM over time in individuals with schizophrenia adherent with LAI in an IPD MA of multiple RCTs.
Aim 2: To identify independent predictors of time to BAMM among a comprehensive set of covariates.
Aim 3: To examine the consistency of the independent predictors of BAMM by comparing the predictors for the primary outcome with those of other measures of treatment failure which will be utilized as secondary outcomes.
Aim 4: To explain potential heterogeneity in the analysis of the pooled sample by conducting subgroup analyses.
Hypothesis 1: A significant proportion of individuals treated with LAI will experience relapse of their psychotic symptoms despite adherence.
Hypothesis 2: Baseline predictors of poor response to antipsychotics will be independent predictors of BAMM (greater baseline severity, shorter period of stability before randomization, greater number of previous hospitalizations, older age, longer DUP, history of medical illness, greater number of previous antipsychotic trials, worsening symptom trajectory).
Hypothesis 3: Predictors of primary outcome will overlap with the predictors of the secondary outcomes.
Hypothesis 4: Heterogeneity will be non-significant when restricting the analyses to subgroup populations

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: Long term antipsychotic use is associated with decreased risk of psychosis relapse, yet adherence with these medications tends to be suboptimal and difficult to assess. The study of the role of antipsychotics in preventing psychosis relapse is often confounded by suboptimal antipsychotic adherence. Objective: To study the incidence and moderators of breaking through antipsychotic maintenance medication (BAMM) in individuals adherent with long acting injectables (LAI). Study Design: Two-Stage individual participant data meta-analysis (IPD MA) of randomized controlled trials (RCTs) with at least one arm of LAI treatment. Participants: Individuals with schizophrenia-spectrum disorders treated for at least 3 months with a LAI as recommended by the package insert. Main Outcome Measure(s): Time to study-defined relapse. Secondary outcome measures will be relapse (categorical), hospitalization, number of psychiatric emergency services/month. Statistical Analysis: We will conduct a 2-Stage IPD MA of RCTs. After calculating the median time to relapse and its 95% confidence interval (95% CI) by the Kaplan-Meier method for each individual trial, we will pool the results following a traditional random-effects model in a 2-stage IPD MA. We will assess the role of independent predictors in the median time to relapse by conducting a maximum likelihood Cox regression model. We will also conduct subgroup analyses to explain potential heterogeneity. A multivariable analysis will be conducted to identify independent predictors of the secondary outcomes.

Brief Project Background and Statement of Project Significance: 

While most individuals with acute psychosis respond to antipsychotics,1 the course of illness is characterized by a relapse-remitting pattern.2 Therefore, relapse prevention is crucial for the long term management of schizophrenia. Though some studies have been able to study factors involved in relapse,2 the role that antipsychotic drugs play in preventing this event is inadequately understood.

Failure to be adherent with antipsychotic drugs is consistently and by far the greatest predictor of relapse.2 Importantly, adherence with antipsychotics is often suboptimal and difficult to assess in individuals with schizophrenia.3 As a result, it is difficult to discriminate between psychosis relapse in individuals with suboptimal exposure to antipsychotics, from psychosis relapse breaking through antipsychotic maintenance medication (BAMM).

In this proposal we aim to study the role of antipsychotics in relapse prevention in a paradigm that is not confounded by non-adherence. We will study BAMM in individuals for whom antipsychotic exposure can be confirmed by the dates of administration of long acting injectable (LAI) antipsychotics. In particular, we will measure the cumulative incidence of BAMM, and its independent clinical predictors. Surprisingly, the literature on BAMM is very limited. To our knowledge, only post-hoc secondary analyses of one trial have examined the role of some sociodemographic and clinical variables involved in this phenomenon.5 In this recent study, Alphs and colleagues found that only duration of illness was an independent predictor of relapse in a sample of individuals treated with LAI risperidone. However, the role of other factors remains to be understood.

A better understanding of BAMM is key to develop more effective interventions for relapse prevention in schizophrenia. While relapse due to insufficient antipsychotic adherence is potentially avoidable, BAMM remains as a barrier for the successful maintenance treatment in schizophrenia. We believe that the proposed study can help to advance the field in several ways. In the first place, by estimating the likelihood of BAMM over time we will determine the magnitude of this problem compared with relapse studies in other populations. Second, these results could help to identify individuals at risk of BAMM, where relapse prevention may be more challenging than in individuals insufficiently exposed to antipsychotics, so early interventions can be developed. Third, the clinical differences between BAMM and continued response can be informative about the antipsychotic effects and the pathophysiology of psychosis, by identifying what factors are associated with sustained antipsychotic efficacy. Fourth, the identification of clinical variables associated with BAMM can inform the design of studies that examine the biological correlates of this phenomenon. Fifth, the convergence of all this data can be used to develop personalized antipsychotic treatment in the future.

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

Inclusion criteria:
• Individuals diagnosed with DSM schizophrenia, schizoaffective, schizophreniform, and psychosis NOS
• Ages 18 to 65
• Participants had to be clinically stable upon randomization however defined by the study
• Randomized controlled clinical trials with at least 1 arm of long acting injectable (LAIs)
• Treatment with a LAI for at least 3 months, with no more than 21 days of cumulative delay in the administration from what is established in the package insert within the first trimester of treatment
• Data is available for our primary or secondary outcome measures
• Data is available for dates of administration of the LAI
• Trial duration of at least 6 months
The source of data will be IPD provided by the YODA project for RCT on long acting risperidone and paliperidone. We plan to combine these with IPD of industry sponsored RCTs on long acting aripiprazole and olanzapine meeting the same inclusion criteria, which will be provided directly to us by the companies. Analyses will be conducted in an intent to treat approach comparing individuals that meet criteria for BAMM (relapse after treatment as defined above) with those with sustained response.

Narrative Summary: 

Antipsychotics are effective in reducing relapses in schizophrenia, yet adherence to these drugs is suboptimal and difficult to assess. This uncertainty limits the study of the role of antipsychotics in preventing relapses. Here, we propose to study the factors involved in breaking through antipsychotic maintenance medication (BAMM) in individuals adherent with long acting injectable antipsychotics, as a paradigm not confounded by suboptimal adherence. Though relapse in suboptimal medication adherence is potentially addressable, BAMM remains a barrier in relapse prevention in psychosis. Characterizing BAMM can help developing more efficacious interventions for relapse prevention.

Project Timeline: 

The proposed dates for completion of the key milestones of the project would be:
• Initiation: By November 2017
• Data cleaning and harmonization: By January 2017
• Completion of analyses: By March 2018
• First manuscript draft: By April 2018
• Submission of manuscript: By June 2018

Dissemination Plan: 

The initial product that we expect to develop is a publication of the IPD MA. We believe that this research would be of interest of a higher tier publication in psychiatry, given the significance of the problem being studied, the innovation of the methods, and the potential advancement to the field of the evidence that will be generated. In addition to publication in peer reviewed journals, we expect to be able to present the findings of this study in various research forums, (conference of the American College of Neuropsychopharmacology, the American Society of Clinical Psychopharmacology, or the International Congress on Schizophrenia Research). Furthermore, we expect that the data generated from this project will inform the design of our own study with primary data, that will study the biological correlates of BAMM, in order to enrich our understanding of this phenomenon. We believe that this publication would serve as the main reference for clinical studies on BAMM, which would then be followed by a whole body of literature on this topic, ranging from its biological to its public health implications.

Bibliography: 

1. Leucht S, Leucht C, Huhn M, Chaimani A, Mavridis D, Helfer B, et al. Sixty Years of Placebo-Controlled Antipsychotic Drug Trials in Acute Schizophrenia: Systematic Review, Bayesian Meta-Analysis, and Meta-Regression of Efficacy Predictors. Am J Psychiatry. 2017 May 25;appi.ajp.2017.1.
2. Robinson D, Woerner MG, Alvir JM, Bilder R, Goldman R, Geisler S, et al. Predictors of relapse following response from a first episode of schizophrenia or schizoaffective disorder. Arch Gen Psychiatry. 1999 Mar;56(3):241–7.
3. Kane JM, Kishimoto T, Correll CU. Non-adherence to medication in patients with psychotic disorders: epidemiology, contributing factors and management strategies. World Psychiatry. 2013 Oct;12(3):216–26.
4. Alphs L, Nasrallah HA, Bossie CA, Fu D-J, Gopal S, Hough D, et al. Factors associated with relapse in schizophrenia despite adherence to long-acting injectable antipsychotic therapy. Int Clin Psychopharmacol. 2016 Jul;31(4):202–9.
5. Stewart LA, Clarke M, Rovers M, Riley RD, Simmonds M, Stewart G, et al. Preferred Reporting Items for a Systematic Review and Meta-analysis of Individual Participant Data: The PRISMA-IPD Statement. JAMA. 2015 Apr 28;313(16):1657.
6. Rubio JM, Correll CU, Inczedy-Farkas G, Birnbaum ML, Kane JM, Leucht S. Efficacy of 42 Pharmacologic Cotreatment Strategies Added to Antipsychotic Monotherapy in Schizophrenia: Systematic Overview and Quality Appraisal of the Meta-analytic Evidence. JAMA Psychiatry. 2017 Jul 1;74(7):675.
7. Burke DL, Ensor J, Riley RD. Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ. Stat Med. 2017 Feb 28;36(5):855–75.
8. Hernández AV, Eijkemans MJC, Steyerberg EW. Randomized Controlled Trials With Time-to-Event Outcomes: How Much Does Prespecified Covariate Adjustment Increase Power? Ann Epidemiol. 2006 Jan;16(1):41–8.
9. Higgins JPT, Green S (editors). Chapter 18. In: Cochrane Handbook for Systematic Reviews of Interventions Version 510 [updated March 2011] The Cochrane Collaboration, 2011 Available from http://handbook.cochrane.org.

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
Participant-level data meta-analysis:
Participant-level data meta-analysis will pool data from YODA Project with other additional data sources
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

The main outcome measure will be relapse, however defined by each study. This outcome will be operationalized as time to event for each participant, counting between date 3 months after randomization to the reported date of relapse.
Secondary outcome measures will be relapse, however defined by each study, categorically defined. Also, psychiatric hospitalization will be used as a categorical variable, whereas psychiatric emergency room visits/month will be used as a continuous variable.
• Study defined relapse (dichotomous)
• Number of ED visits/month during treatment trial (continuous)
• Psychiatric hospitalization during trial (dichotomous)
• Psychiatric Hospitalization during trial (date)

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

For a full list and description of covariates see supplementary materials:
• LAI dose
• Dose trajectory
• Sex
• Race
• Age
• DSM diagnosis
• Duration of untreated psychosis
• Total illness duration
• Time since last hospitalization
• Number of previous hospitalizations lifetime
• Number of hospitalizations in 2 years prior to randomization
• Medical Hospitalization during trial
• Medical comorbidities at baseline
• Number of prior antipsychotic trials different to the antipsychotic being randomized to
• Co-treatment with oral antipsychotic
• Duration of use of standing concomitant oral antipsychotic
• Use of concomitant psychotropic medications other than antipsychotics
• Use of concomitant chronic non-psychotropic medication
• Psychiatric comorbidities
• Regular cannabis use
• Regular nicotine smoking
• Baseline and trajectory of BPRS/PANSS
• Baseline and trajectory of CGI score
• Baseline and trajectory of depressive symptoms
• Baseline and trajectory of functioning and quality of life
• History of psychological trauma
• Baseline BMI
• Psychosocial stressors during trial
• Side effect
• Site

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

In addition to the covariates above, we will need the following variables in order to build the model:
• Dates of administration of LAIs
• Dates of study defined relapse
• Dose of LAIs at each administration
• BPRS/PANSS score at each assessment
• CGI score at each assessment
• Depressive symptoms score at each assessment
• Functioning scale score at each assessment
• Quality of life scale score at each assessment

Statistical Analysis Plan: 

Analysis of main outcome:
The general analytic approach will be to conduct a IPD MA comparing individuals with BAMM as defined above with sustained response through trial course, following the recommendations stated in the PRISMA-IPD Statement.5 We believe that this method will have advantages over study-based meta-analyses to test the aforementioned hypotheses, given the heterogeneity found in treatment response in schizophrenia.7 In this proposal, we aim to conduct a MA combining IPD from the industry sponsored RCTs on risperidone long acting injectable and paliperidone palmitate provided by the YODA project, along with IPD of the industry sponsored RCTs for the other LAIs in the market (i.e., aripiprazole monohydrate, aripiprazole lauroxil, olanzapine palmoate), which will be provided to us directly by the companies. All the IPD data from sources other than YODA will be uploaded to the secure platform, where the analyses will be conducted using SAS. We will choose a 2-stage method for the IPD MA, which is often preferred for using standard meta-analytic procedures in the second stage and producing virtually the same results than a single stage method.8 In the first stage we will proceed to calculate for each RCT the median time to relapse and its respective 95% confidence intervals using the Kaplan-Meier method, after excluding patients that do not complete the first 3 months of adherence and stability. We will next calculate again for each independent RCT the effects of the covariates using a maximum likelihood estimation to fit a Cox regression model. Based on the recommendation by Hernández et al.,9 we will adjust for known predictors of relapse, including age of illness onset, duration of illness, baseline functioning score, baseline PANSS/BPRS score, cannabis use, and number of previous hospitalizations, as predictive covariates. Once we have calculated the within group differences for each trial, we will combine the effects in each trial using the standard meta-analytic method of random-effects, both to calculate the pooled median time to relapse, and the pooled effects of the covariates.10 We will measure heterogeneity using the I2.
Subgroup analysis:
One of the advantages of IPD MA is that it allows for subgroup analysis that may not be possible in individual trials due to small sample size. Since we will be conducting a Cox regression to identify independent predictors, we will restrict the use of subgroup analysis to identify sources of heterogeneity, if I2 >50%. In the event of significant heterogeneity, we will conduct subgroup analyses for the variables that were significant in the Cox regression. We will then compare the I2 for both the total group excluding the subgroup of interest and the subgroup itself, to find the removal of which subgroups reduces heterogeneity, therefore explaining it.
Analysis of secondary outcomes:
We will conduct the analyses of the secondary outcomes following the same structure of a 2-stage IPD MA as we described above for the primary outcome. In the first step, we will calculate for each RCT its risk ratio (RR) for categorical variables (study defined relapse, psychiatric hospitalization during trial) and standard mean deviation (SMD) for continuous variables, using logistic regression analyses, and adjusting for the same covariates as in the main outcome analyses. In the second stage, we will combine the effect estimates and variance for each trial (within trial estimates) and combine them in a usual random-effects meta-analysis, for the estimation of both the RR/SMD for each outcome, as well as the effects of the covariates in each model. We will examine the same subgroup analyses as described above for the main outcome.

How did you learn about the YODA Project?: 
Software Used: 
R
Associated Trials: 
<ol><li><a href="/node/181">NCT00589914 - R092670PSY3006 - A Randomized, Double-Blind, Parallel-Group, Comparative Study of Flexible Doses of Paliperidone Palmitate and Flexible Doses of Risperidone Long-Acting Intramuscular Injection in Subjects With Schizophrenia</a></li><li><a href="/node/182">NCT00604279 - R092670PSY3008 - A Randomized, Open-Label, Parallel Group Comparative Study of Paliperidone Palmitate (50, 100, 150 mg eq) and Risperidone LAI (25, 37.5, or 50 mg) in Subjects with Schizophrenia</a></li><li><a href="/node/190">NCT00590577 - R092670PSY3007 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Dose Response Study to Evaluate the Efficacy and Safety of 3 Fixed Doses (25 mg eq., 100 mg eq., and 150 mg eq.) of Paliperidone Palmitate in Subjects With Schizophrenia</a></li><li><a href="/node/191">NCT00111189 - R092670PSY3001 - A Randomized Double-blind Placebo-controlled Parallel Group Study Evaluating Paliperidone Palmitate in the Prevention of Recurrence in Patients With Schizophrenia. Placebo Consists of 20% Intralipid (200 mg/mL) Injectable Emulsion</a></li><li><a href="/node/192">NCT00210717 - R092670PSY3002 - A Randomized, Double-Blind, Parallel Group, Comparative Study of Flexibly Dosed Paliperidone Palmitate (25, 50, 75, or 100 mg eq.) Administered Every 4 Weeks and Flexibly Dosed RISPERDAL CONSTA (25, 37.5, or 50 mg) Administered Every 2 Weeks in Subjects With Schizophrenia</a></li><li><a href="/node/193">NCT00119756 - R092670PSY3005 - A Randomized, Crossover Study to Evaluate the Overall Safety and Tolerability of Paliperidone Palmitate Injected in the Deltoid or Gluteus Muscle in Patients With Schizophrenia</a></li><li><a href="/node/194">NCT00210548 - R092670PSY3003 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Dose-Response Study to Evaluate the Efficacy and Safety of 3 Fixed Doses (50 mg eq., 100 mg eq., and 150 mg eq.) of Paliperidone Palmitate in Subjects With Schizophrenia</a></li><li><a href="/node/195">NCT00101634 - R092670PSY3004 - A Randomized, Double-blind, Placebo-controlled, Parallel-group, Dose-response Study to Evaluate the Efficacy and Safety of 3 Fixed Doses (25 mg eq, 50 mg eq, and 100 mg eq) of Paliperidone Palmitate in Patients With Schizophrenia</a></li><li><a href="/node/562">NCT00216476 - RISSCH3001 - CONSTATRE: Risperdal® Consta® Trial of Relapse Prevention and Effectiveness</a></li><li><a href="/node/563">NCT00216580 - RIS-PSY-301 - An Open-label Trial of Risperidone Long-acting Injectable in the Treatment of Subjects With Recent Onset Psychosis</a></li><li><a href="/node/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/867">NCT01529515 - R092670PSY3012  - A Randomized, Multicenter, Double-Blind, Relapse Prevention Study of Paliperidone Palmitate 3 Month Formulation for the Treatment of Subjects With Schizophrenia</a></li><li><a href="/node/868">NCT01193153 - R092670SCA3004 - A Randomized, Double-Blind, Placebo-Controlled, Parellel-Group Study of Paliperidone Palmitate Evaluating Time to Relapse in Subjects With Schizoaffective Disorder </a></li><li><a href="/node/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/3773">NCT01258920 - PALM-JPN-5 - A Long-Term, Open-Label Study of Flexibly Dosed Paliperidone Palmitate Long-Acting Intramuscular Injection in Japanese Patients With Schizophrenia</a></li><li><a href="/node/3805">NCT00369239 - RISSCH4043 - Is Premorbid Functioning a Predictor of Outcome in Patients With Early Onset Psychosis Treated With Risperdal Consta?</a></li><li><a href="/node/3855">NCT00495118 - RIS-INT-80 - Risperidone Depot (Microspheres) in the Treatment of Subjects With Schizophrenia or Schizoaffective Disorder - an Open-label Follow-up Trial of RIS-INT-62 and RIS-INT-85</a></li><li><a href="/node/3857">NCT00236457 - RIS-INT-62 - Randomized, Multi-center, Open Label Trial Comparing Risperidone Depot (Microspheres) and Olanzapine Tablets in Patients With Schizophrenia or Schizoaffective Disorder</a></li><li><a href="/node/3858">NCT00236587 - RIS-USA-265 - An Open Label, Long Term Trial of Risperidone Long Acting Microspheres in the Treatment of Patients Diagnosed With Schizophrenia</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2017

2017-1856

Project Title: 
Impact of Age on Safety and Efficacy of Biologic Therapy for Inflammatory Bowel Disease
Specific Aims of the Project: 

1) The primary aim of this study will be to compare absolute and relative clinical efficacy rates of biologic therapy across the strata of age (>60 yrs versus < 60 yrs). Absolute clinical efficacy will be assessed by the absolute difference in rates of clinical remission between older patients with any biologic therapy and placebo compared to younger patients with any biologic therapy and placebo. Relative efficacy will be assessed as the crude difference between older and younger patients on biologic therapy.
2) The secondary aim of this study will be to compare absolute and relative safety rates of biologic therapy across the strata of age (>60 yrs versus <60 yrs). Safety outcomes of interest will include serious adverse events, infection related adverse events and malignancy. Absolute safety rates will be assessed by the absolute difference in rates of events between older patients on any biologic therapy and placebo compared to younger patients on any biologic therapy and placebo. Relative safety rates will be assessed as the crude difference between older and younger patients on biologic therapy.
3) Additional endpoints of interest will include absolute and relative clinical response, endoscopic healing, adverse events, rates of antibody formation, and quality of life

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: The elderly represent a high risk group of patients with IBD. As such, efficacy and safety data of biologic therapy is needed. Objective: Define efficacy and safety of biologics in adults > 60 years. Study Design: Participants will be grouped by cases (adults > 60 years) and controls (adults < 60 years). The primary outcome evaluated will be absolute and relative clinical efficacy. Secondary endpoints will include absolute and relative clinical response, endoscopic healing, adverse events, antibody formation, and health-related quality of life. Clinical response will be defined as a decrease in CDAI by ≥ 100 in CD and Mayo score >2 with all sub-scores ≤ 1 in UC. Endoscopic healing will be assessed by the Mayo endoscopic sub-score for UC and gross endoscopic findings for CD. Health-related quality of life will be measured by the short form of the IBD Questionnaire (SIBD-Q) score. Participants: Adults > 60 years will be included as cases. All other participants (<60 years) will be included as controls. Main Outcome Measures: The main outcome measures include clinical efficacy, clinical response, and rates of adverse events. Additional outcomes of interest include endoscopic healing, antibody formation, and health-related quality of life. Statistical Analysis: Comparison of variables will be performed by t, Mann Whitney, Chi Square or Fisher Exact Test, as appropriate. Random effects model with meta regression, adjusting for potential confounders, will be used to examine the effect of age on efficacy and safety of biologic therapies in IBD.

Brief Project Background and Statement of Project Significance: 

It is estimated that 10 to 30% of patients with IBD are over the age of 60.(1) These patients represent an important group for further study in IBD because their management requires additional considerations. First, the disease presentation and course may be different across age groups. A population based cohort study of IBD in France, EPIMAD, investigated differences in the natural history of IBD by age.(2) Results of this study demonstrated that older patients with CD tended to present more often with rectal bleeding and anal fistulas whereas younger patients tended to present more often with diarrhea and abdominal pain. In addition, the disease distribution in CD was more often colonic and the behavior more often inflammatory in older adults. Finally, the rate of disease behavior progression over 15 years was relatively low (9%), which may suggest a more indolent course. Alternatively, older patients with UC tended to present less often with rectal bleeding and abdominal pain and their disease distribution was more commonly left sided with 16% having some level of disease progression. Next, with increasing age comes the potential for additional complications both independently and related to increasing co-morbidities and polypharmacy.(1) Older patients with IBD-related hospitalization had an independently higher mortality when compared to younger patients, regardless of concurrent co-morbidity (OR 3.91).(3) These results highlight the independently deleterious effect age can have in IBD. In addition, older patients may have increasing cardiovascular, pulmonary, or metabolic disease, which can add complexity to decision making. Finally, the existing literature on biologic use in the elderly is limited. A retrospective review of anti-TNF efficacy demonstrated similar rates of clinical remission among those older and those younger than 65.(4) Lobaton, et al also demonstrated equivalent anti-TNF efficacy over the long term but showed lower efficacy in the short term.(5) Alternatively, Ananthakrishnan et al found lower efficacy in older patients when compared to younger patients.(6) Interpretation of retrospective studies is difficult and maintenance anti-TNF therapy is still reported to be low in the elderly; 9% in CD and 1% in UC.(1) Furthermore, older patients have been demonstrated to be three times more likely to stop therapy, with 70% discontinuing therapy after just over two years.(6) Low rates of use may be related to hesitancy by prescribers to use immune suppressive drugs in this higher risk population. Such concerns are supported by a reported rate of infectious adverse events of 11% in the elderly on anti-TNF therapy.(4) Interpretation of primary clinical trial data may offer additional insight into clinical efficacy and safety. Unfortunately, published clinical trial data in the elderly is limited by the small absolute number of participants as well as the lower average age of participants.(7) In summary, there is a need for more data evaluating the efficacy and safety of biologic treatment in the elderly and composite clinical trial data may allow for more accurate reflections of efficacy and safety.

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 therapy in CD or UC. Based on a preliminary evaluation of the data available on the website, we expect to procure data related to infliximab, golimumab, and ustekinumab use. Additional drugs of interest, should they become available in the interim, would include adalimumab, certolizumab, vedolizumab, mercaptopurine, azathioprine, and/or methotrexate. All patients included in the original clinical trial data will be included in this sub-analysis. No patients will be excluded.

Narrative Summary: 

Inflammatory Bowel Disease (IBD) encompasses two immune-mediated gastrointestinal tract diseases, Crohns Disease (CD) and Ulcerative Colitis (UC). Untreated disease can lead to chronic sequela of inflammation such as strictures, fistulas, dysplasia, and/or need for bowel resection. The incorporation of biologic therapy into practice has improved medical management of IBD. However, there is limited data on the efficacy and safety of these medications in high risk groups. This study proposes utilizing the repository of biologic trial data to evaluate the efficacy and safety of these medications in the elderly.

Project Timeline: 

Anticipated Project Start Date: August 1, 2017
Analysis Completion Date: March 31, 2018
Report of Results to YODA: May 31, 2018
Date of First Manuscript Draft: June 1, 2018
Date of Manuscript Submission: August 1, 2018

Dissemination Plan: 

The expected audience for this work includes practicing general gastroenterologists and IBD sub-specialists. Potential journals for submission include Inflammatory Bowel Disease and the Journal of Crohn’s and Colitis.

Bibliography: 

1. Taleban S, Colombel JF, Mohler MJ, et al. Inflammatory Bowel Disease and the Elderly: A Review. J Crohns Colitis. 2015; 9(6):507-515.
2. Charpentier C, Salleron J, Savoye G, et al. Natural history of elderly-onset inflammatory bowel disease: a population-based cohort study. Gut. 2014; 63:423-432.
3. Ananthakrishnan AN, McGinley EL, Binion DG. Inflammatory Bowel Disease in the Elderly Is Associated With Worse Outcomes: A National Study of Hospitalizations. Inflamm Bowel Dis. 2009; 15(2):182-189.
4. Cottone M, Kohn A, Daperno M, et al. Advanced Age Is an Independent Risk Factor for Severe Infections and Mortality in Patients Given Anti-Tumor Necrosis Factor Therapy for Inflammatory Bowel Disease. Clin Gastroenterol Hepatol. 2011; 9(1):30-35.
5. Lobaton T, Ferrante M, Rutgeerts P, et al. Efficacy and safety of anti-TNF therapy in elderly patients with inflammatory bowel disease. Aliment Pharmacol Ther. 2015; 42(4): 441-451.
6. Desai A, Zator ZA, de Silva P, et al. Older age is associated with higher rate of discontinuation of anti-TNF therapy in patients with Inflammatory Bowel Disease. Inflamm Bowel Dis. 2013; 19(2):309-315
7. Katz S, Pardi DS. Inflammatory Bowel Disease of the Elderly: Frequently Asked Questions (FAQs). Am J Gastroenterol. 2011; 106(11): 1889-1897.

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
New research question to examine treatment safety
Supplementary Material: 
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

The main outcome measure evaluated will be clinical remission. Clinical remission will be defined as a CDAI score less than 150 for CD and a Mayo Score less than 2 for UC. Clinical remission will be measured as an absolute difference (older patients with any biologic therapy versus placebo compared to younger patients with any biologic therapy versus placebo) and relative difference (older patient on any biologic therapy versus younger patient on any biologic therapy). The secondary outcome measure will be safety. Safety outcomes of interest will include serious adverse events, infection related adverse events, and malignancy. Absolute safety rates will be assessed by the absolute difference in rates of events between older patients on any biologic therapy and placebo compared to younger patients on any biologic therapy and placebo. Relative safety rates will be assessed as the crude difference between older and younger patients on biologic therapy.

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

The main predictor variable will be age.

• Cases will be defined as adults greater than or equal to 60 years of age.
• Controls will be defined as adults less than 60 years of age.

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 absolute and relative differences in clinical response, endoscopic healing, antibody formation, and health-related quality of life. Clinical response will be defined as a decrease in CDAI by greater than or equal to 100 in CD and 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 and absence of ulcers in CD. 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. Random effects model with meta regression of potential confounders (type of biologic [anti-TNF versus IL-23], type of disease [CD versus UC], and presence or absence of concomitant immunosuppression) will be used to examine the effect of age on clinical efficacy and safety of biologic therapies in the management of IBD.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/156">NCT00036439 - C0168T37 - A Randomized, Placebo-controlled, Double-blind Trial to Evaluate the Safety and Efficacy of Infliximab in Patients With Active Ulcerative Colitis</a></li><li><a href="/node/157">NCT00096655 - C0168T46 - A Randomized, Placebo-controlled, Double-blind Trial to Evaluate the Safety and Efficacy of Infliximab in Patients With Active Ulcerative Colitis</a></li><li><a href="/node/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/166">NCT00487539 - C0524T17 - A Phase 2/3 Multicenter, Randomized, Placebo-controlled, Double blind Study to Evaluate the Safety and Efficacy of Golimumab Induction Therapy, Administered Subcutaneously, in Subjects with Moderately to Severely Active Ulcerative Colitis</a></li><li><a href="/node/353">NCT00207662 - C0168T21 - ACCENT I - A Randomized, Double-blind, Placebo-controlled Trial of Anti-TNFa Chimeric Monoclonal Antibody (Infliximab, Remicade) in the Long-term Treatment of Patients With Moderately to Severely Active Crohn's Disease</a></li><li><a href="/node/354">NCT00207766 - C0168T26 - ACCENT II - A Randomized, Double-blind, Placebo-controlled Trial of Anti-TNF Chimeric Monoclonal Antibody (Infliximab, Remicade) in the Long Term Treatment of Patients With Fistulizing CROHN'S Disease</a></li><li><a href="/node/355">NCT00004941 - C0168T20 - A Placebo-controlled, Repeated-dose Study of Anti-TNF Chimeric Monoclonal Antibody (cA2) in the Treatment of Patients with Enterocutaneous Fistulae as a Complication of Crohn’s Disease</a></li><li><a href="/node/455">NCT00537316 - P04807 - Efficacy & Safety of Infliximab Monotherapy Vs Combination Therapy Vs AZA Monotherapy in Ulcerative Colitis (Part 1) Maintenance Vs Intermittent Therapy for Maintaining Remission (Part 2)</a></li><li><a href="/node/755">NCT01551290 - CR018769 - A Phase 3, Multicenter, Randomized, Double-Blind, Placebo-Controlled Study Evaluating the Efficacy and Safety of Infliximab in Chinese Subjects With Active Ulcerative Colitis</a></li><li><a href="/node/984">NCT01190839 - REMICADECRD3001 - Prospective, Multicenter, Randomized, Double-Blind, Placebo-Controlled Trial Comparing REMICADE (Infliximab) and Placebo in the Prevention of Recurrence in Crohn's Disease Patients Undergoing Surgical Resection Who Are at Increased Risk of Recurrence</a></li><li><a href="/node/985">NCT00269854 - C0168T16 - A Placebo-Controlled, Dose-Ranging Study Followed by a Placebo-Controlled, Repeated-Dose Extension of Anti-TNF Chimeric Monoclonal Antibody (cA2) in the Treatment of Patients With Active Crohn's Disease</a></li><li><a href="/node/986">C0168T16 - Efficacy and safety of retreatment with anti-tumor necrosis factor antibody (infliximab) to maintain remission in Crohn's disease.</a></li><li><a href="/node/1129">NCT00771667 - C0743T26 - A Phase 2b, Multicenter, Randomized, Double-blind, Placebo-controlled, Parallel Group Study to Evaluate the Efficacy and Safety of Ustekinumab Therapy in Subjects With Moderately to Severely Active Crohn's Disease Previously Treated With TNF Antagonist Therapy</a></li><li><a href="/node/1133">NCT01369329 - CNTO1275CRD3001 - A Phase 3, Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter Study to Evaluate the Safety and Efficacy of Ustekinumab Induction Therapy in Subjects With Moderately to Severely Active Crohn's Disease Who Have Failed or Are Intolerant to TNF Antagonist Therapy (UNITI-1)</a></li><li><a href="/node/1134">NCT01369342 - CNTO1275CRD3002 - A Phase 3, Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter Study to Evaluate the Safety and Efficacy of Ustekinumab Induction Therapy in Subjects With Moderately to Severely Active Crohn's Disease (UNITI-2)</a></li><li><a href="/node/1286">NCT00488631 - C0524T18 - A Phase 3 Multicenter, Randomized, Placebo-controlled, Double-blind Study to Evaluate the Safety and Efficacy of Golimumab Maintenance Therapy, Administered Subcutaneously, in Subjects With Moderately to Severely Active Ulcerative Colitis</a></li><li><a href="/node/1361">NCT01369355 - CNTO1275CRD3003 - A Phase 3, Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter Study to Evaluate the Safety and Efficacy of Ustekinumab Maintenance Therapy in Subjects With Moderately to Severely Active Crohn's Disease</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2017

2017-1846

Project Title: 
Discontinuation symptoms in antipsychotics: Individual patient level analyses of randomized controlled trials
Specific Aims of the Project: 

Primary objective:
a. Evaluate whether discontinuation symptoms occur after rapid
discontinuation of the prestudy antipsychotic.

Secondary objectives:
b. Evaluate whether discontinuation symptoms are linked to the
type of discontinued antipsychotic (e.g. olanzapine, amisulpride,
risperidone, etc.)
c. Evaluate whether discontinuation symptoms can be predicted by
specific receptor affinities (Ki values) of the discontinued
antipsychotic3.
d. Evaluate whether discontinuation symptoms can be differentiated
from early recurrence of major symptoms (i.e. are certain AEs especially predictive before recurrence of psychotic or manic symptoms or disruptive behaviour)?)

Primary endpoint:
a. Total AE rate and recurrence of major symptoms (psychotic or
manic symptoms or disruptive behaviour).

Secondary endpoints:
b. Association between type of discontinued antipsychotic and AE
rates and recurrence of major symptoms respectively.
c. Interdependence network8 between receptor affinities and AE
rates and recurrence of major symptoms respectively.
d. Most predictive AEs before recurrence of major symptoms.

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: Antipsychotics are mainly used in the treatment of schizophrenia and other mental disorders, such as bipolar disorder or disruptive behaviour disorders (main indication for antipsychotics in children2). Discontinuation and switching the type of substance may lead to relevant symptoms interfering with the safety and adherence of psychiatric treatment. Especially rapid discontinuation of antipsychotics which functionally inhibit or stimulate receptors may lead to psychiatric and other somatic symptoms1,3.
Objective: Our goal is to systematically assess the full range of discontinuation symptoms.
Study Design: We plan to investigate the relationship between adverse events (AEs) and discontinuation of an antipsychotic by performing meta-analyses of individual participant data in the placebo groups of RCTs following patients with versus without previous medication.
Participants: Schizophrenia, bipolar disorder, schizoaffective disorder and children with disruptive behaviour disorders.
Main outcome measure: Our main outcome measure will be total AEs and recurrence of major symptoms (psychotic or manic symptoms or disruptive behaviour) in two placebo subgroups. The target group consists of patients who discontinued an antipsychotic just before receiving the placebo and the control group consists of patients who had not recently been taking antipsychotics before receiving the placebo.
Statistical analysis: The relationship between T0 and T1 scores for the two placebo subgroups will be examined with a mixed model of repeated measures and Kaplan-Meier estimator.

Brief Project Background and Statement of Project Significance: 

Antipsychotic drugs are a heterogeneous group of compounds with a wide range of receptor affinities and diverse functional effects4. These substances may cause a variety of side effects in patients5. Therefore, providing the appropriate antipsychotic substance is a complex process1. The process frequently includes discontinuation and switching of compounds and may be accompanied or even initiated by AEs comprising cholinergic, dopaminergic, serotonergic, histaminergic and adrenergic rebound phenomena3. During switching, AEs may be caused by the current drug but could also be related to the cessation of a prior drug. Differentiating the cause for the AEs requires knowledge of the discontinuation symptoms caused by the specific compound. Additionally, there is a large number of patients who show poor adherence of antipsychotic substances especially during stable phases of illness or at the beginning of relapse (e.g. 43% of schizophrenic patients had at least one year of poor adherence over four years)6. Therapeutic strategies and treatment adherence could be significantly optimized if clinicians and patients were well
informed about potential discontinuation symptoms. This study could have a major impact on health of patients as systematic analyses of discontinuation symptoms in antipsychotics could help to identify discontinuation symptoms and may help to promote the development of innovative therapeutic strategies and guidelines in this field. This would have very practical implications for the individual patient as rapid discontinuation of an antipsychotic without professional supervision is very frequent in clinical routine7. The importance of this study is highlighted by the lack of systematic assessment of discontinuation symptoms in RCTs after rapid and complete discontinuation of antipsychotic treatment1. This study will be a first step to implement further research into which factors are predictive for occurrence of discontinuation symptoms in an individual and in long term develop treatment strategies for discontinuation syndromes.

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

We will merge data from the placebo groups in RCTs on antipsychotic treatment of patients with schizophrenia, bipolar disorder and schizoaffective disorder. Children with disruptive behaviour disorders will be included in the analysis as a separate group to investigate discontinuation symptoms in children9.

For oral antipsychotics, the placebo group will be divided into two subgroups:
A. Target group: Patients who have just stopped taking the antipsychotic no longer than 3 days before entering the placebo group will be compared
with B.
B. Control group: All patients who have not been taking medication for
more than 1 month before entering the placebo group.

For long-acting injectable antipsychotics, the placebo group will also be divided into two subgroups:
C. Target group: Patients who should have had their last scheduled injection no longer than 1 week before entering the placebo group will be compared with D.
D. Control group: Patients who have not been receiving long-acting injectables in the last 3 month (and no oral antipsychotic for more than 1 month) before entering the placebo group.
Primary target: total AE rate during the first 12 weeks.

Narrative Summary: 

Avoiding the recurrence of major symptoms and rebound phenomena after discontinuation or switching of antipsychotics is a key factor when planning a safe and successful therapy. Rebound phenomena and recurrence of major symptoms like psychotic or manic symptoms or disruptive behaviour are among the known risks when discontinuing antipsychotics but the systematic evaluation have been scarcely studied1.
We intend to assess the complete spectrum of discontinuation symptoms in patients with schizophrenia, schizoaffective disorder, bipolar disorder and children with disruptive behaviour disorders treated with antipsychotics in the placebo group of randomized controlled trials.

Project Timeline: 

Immediately after the data is available the project will start and the study plan will be published online (8/2017). The analysis will be completed six months later (2/2018). The manuscript will be drafted and submitted after four months (06/2018). The publication is planned for 08/2018. The YODA project will be informed about the completion of each milestone and reports will be made available.

Dissemination Plan: 

To benefit both health professionals and patients we will present the study at internationally accredited conferences (e.g. symposia at the WPA) and make the study available in major medical journals (e.g. JAMA Psychiatry, American Journal of Psychiatry, Lancet Psychiatry). Based on our results we will develop and validate a questionnaire to assess the risk of discontinuation symptoms. Patients will be directly affected as national and international treatment guidelines will be influenced.

Bibliography: 

1. Cerovecki, A. et al. Withdrawal Symptoms and Rebound Syndromes Associated with Switching and Discontinuing Atypical Antipsychotics: Theoretical Background and Practical Recommendations. CNS Drugs 27, 545–572 (2013).
2. Penfold, R. B. et al. Use of Antipsychotic Medications in Pediatric Populations: What do the Data Say? Curr Psychiatry Rep 15, 13–16 (2013).
3. Correll, C. U. From receptor pharmacology to improved outcomes:
individualising the selection, dosing, and switching of antipsychotics.
European Psychiatry 25, S12–S21 (2010).
4. Leucht, S. et al. Comparative efficacy and tolerability of 15 antipsychotic
drugs in schizophrenia: a multiple-treatments meta-analysis. Lancet 382,
951–962 (2013).
5. Hasan, A. et al. World Federation of Societies of Biological Psychiatry
(WFSBP) Guidelines for Biological Treatment of Schizophrenia, Part 2: Update 2012 on the long-term treatment of schizophrenia and management of antipsychotic-induced side effects. The World Journal of Biological Psychiatry 14, 2–44 (2013).
6. Valenstein, M. et al. Antipsychotic adherence over time among patients receiving treatment for schizophrenia: A retrospective review. J Clin Psychiatry 67, 1542–1550 (2006).
7. Fava, G. A., et al. Withdrawal Symptoms after Selective Serotonin Reuptake Inhibitor Discontinuation: A Systematic Review. Psychother Psychosom 84, 72–81 (2015).
8. Barabási, A. L., Gulbahce, N. & Loscalzo, J. Network medicine: a network- based approach to human disease. Nature Reviews Genetics (2011). doi:10.1038/nrg2918
9. Lu, H. & Rosenbaum, S. Developmental pharmacokinetics in pediatric populations. J Pediatr Pharmacol Ther 19, 262–276 (2014).
10. Kane, J. et al. Treatment of schizophrenia with paliperidone extended- release tablets: A 6-week placebo-controlled trial. Schizophrenia Research 90, 147–161 (2006).
11. Hough, D. et al. Paliperidone palmitate maintenance treatment in delaying the time-to-relapse in patients with schizophrenia: A randomized, double- blind, placebo-controlled study. Schizophrenia Research 116, 107–117 (2010).
12. Little, R. J. et al. The Prevention and Treatment of Missing Data in Clinical Trials. N Engl J Med 367, 1355–1360 (2012).

What is the purpose of the analysis being proposed? Please select all that apply.: 
Research that confirms or validates previously conducted research on treatment safety
Participant-level data meta-analysis:
Participant-level data meta-analysis uses only data from YODA Project
Supplementary Material: 
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

For oral application, the main outcome is change in the total AE rate and recurrence of major symptoms (psychotic or manic symptoms or disruptive behaviour) from baseline (T0) to four weeks (T1). All assessment time points in this timeframe will be included (i.e. the primary target is total AE rate and recurrence of major symptoms during the first 4 weeks).
For long-acting injectables, the main outcome is change in the total AE rate and recurrence of major symptoms from baseline (T0) to twelve weeks (T1). All assessment time points in this timeframe will be included (i.e. the primary target is total AE rate and recurrence of major symptoms during the first twelve weeks).
Oral and long-acting injectable antipsychotics will be calculated separately. All detected AEs will be included in the analyses. General AEs (e.g. vegetative dysregulation) and recurrence of psychotic or manic symptoms or disruptive behaviour will be calculated separately.

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

The main predictor will allow us to investigate the relationship between antipsychotic treatment (substance taken before entering the placebo group) and discontinuation symptoms. The main predictor is the rapid discontinuation of an oral or long-acting injectable antipsychotic.
Rapid discontinuation of the oral application will be defined as discontinuation less than three days before entering the placebo group10.
Rapid discontinuation of a long-acting injectable application will be defined as the next scheduled injection less than one week before entering the placebo group (e.g. 28 days after a four week depot antipsychotic)11.
These two groups will be compared to patients in the placebo group without rapid discontinuation of antipsychotics as described in the previous section “Data Source and Inclusion Criteria to be used to define the patient sample for your study”.

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

We will include additional variables/characteristics associated with occurrence of AEs and examine them for their possible confounding effect including age, sex, weight, duration and dose of antipsychotic application, previous medication, measures of psychopathology (PANSS/YMRS/Conduct Problem Subscale/etc.), duration of illness, duration of untreated psychosis, number of hospitalizations, etc. The Ki values were previously summarized (summary by Correll, p. 15, table 2)3 and will be implemented according to this study. Receptor types and corresponding rebound syndromes were also defined (definition by Correll, p. 18, table 3)3 and these definitions will be used in our study. The number needed to harm (NNH) and network analysis8 of receptor affinities will be calculated separately for both types of application (oral and long-acting injectable).

Statistical Analysis Plan: 

A mixed model of repeated measures (MMRM) and Kaplan-Meier estimator will be used to investigate the relationship between rapid discontinuation of an antipsychotic and the total AE rate and recurrence of major symptoms (psychotic or manic symptoms or disruptive behaviour) in an individual participant data meta-analysis. Baseline score (T0) of AE rates will be determined at the time when the participant is included in the study and the post-baseline score (T1) is determined at the last time point of the included timeline. Recurrence of psychotic or manic symptoms or disruptive behaviour will be determined between T0 and T1 (measured as change in score PANSS/YMRS/Conduct Problem Subscale/etc.) The within-subject factor is “time” and the between-subjects factor is “rapid discontinuation of an antipsychotic” (Yes/No) and the model will be tested adjusted and unadjusted for confounders (e.g. age, sex, duration of application, etc.).
The relationship between type of antipsychotic and the AE rates and recurrence of major symptoms will be assessed with multinominal logistic regression. The relationship between Ki values and the AE rate of the corresponding rebound syndrome and recurrence of major symptoms will be investigated with network analysis8 and ordinal logistic regression. Ki values will be treated as independent variables and the AE rate of the corresponding rebound syndrome and recurrence of major symptoms as dependent variable. The potential predictive value of an AE for a consecutive psychotic relapse will be investigated with multinominal logistic regression.
Missing data will be treated as recommended by Little et al.12 We will register if reasons for missing data were documented and develop a primary set of assumptions about the cause for missing data12. The primary set of assumptions will be followed by a matching statistically valid analysis (e.g. estimating-equation methods) and robustness tested with a sensitivity analysis12.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/167">NCT00488319 - R076477PSZ3002 - A 2-Year, Open-Label, Single-Arm Safety Study of Flexibly Dosed Paliperidone Extended Release (1.5-12 mg/day) in the Treatment of Adolescents (12 to 17 Years of Age) With Schizophrenia</a></li><li><a href="/node/173">NCT01009047 - R076477PSZ3003 - A Randomized, Multicenter, Double-Blind, Active-Controlled, Flexible-Dose, Parallel-Group Study of the Efficacy and Safety of Prolonged Release Paliperidone for the Treatment of Symptoms of Schizophrenia in Adolescent Subjects, 12 to 17 Years of Age </a></li><li><a href="/node/174">NCT00645099 - R076477SCH3020 - A Prospective Randomized Open-label 6-Month Head-To-Head Trial to Compare Metabolic Effects of Paliperidone ER and Olanzapine in Subjects With Schizophrenia</a></li><li><a href="/node/175">NCT00518323 - R076477PSZ3001 - A Randomized, Multicenter, Double-Blind, Weight-Based, Fixed-Dose, Parallel-Group, Placebo-Controlled Study of the Efficacy and Safety of Extended Release Paliperidone for the Treatment of Schizophrenia in Adolescent Subjects, 12 to 17 Years of Age</a></li><li><a href="/node/177">NCT01606228 - R076477SCH3033 - An Open-Label Prospective Trial to Explore the Tolerability, Safety and Efficacy of Flexibly-Dosed Paliperidone ER among Treatment-Naive and Newly Diagnosed Patients with Schizophrenia</a></li><li><a href="/node/178">NCT00334126 - R076477SCH3015 - A Randomized, Double-blind, Placebo-controlled, Parallel Group Study to Evaluate the Efficacy and Safety of Paliperidone ER Compared to Quetiapine in Subjects With an Acute Exacerbation of Schizophrenia</a></li><li><a href="/node/179">NCT00086320 - R076477-SCH-301 - A Randomized, Double-blind, Placebo-controlled, Parallel-group Study With an Open-label Extension Evaluating Paliperidone Extended Release Tablets in the Prevention of Recurrence in Subjects With Schizophrenia</a></li><li><a href="/node/180">NCT00650793 - R076477-SCH-703 - A Randomized, DB, PC and AC, Parallel Group, Dose-Response Study to Evaluate the Efficacy and Safety of 3 Fixed Dosages of Extended Release OROS Paliperidone (6, 9, 12 mg/Day) and Olanzapine (10 mg/Day), With Open-Label Extension, in the Treatment of Subjects With Schizophrenia - Open Label Phase</a></li><li><a href="/node/181">NCT00589914 - R092670PSY3006 - A Randomized, Double-Blind, Parallel-Group, Comparative Study of Flexible Doses of Paliperidone Palmitate and Flexible Doses of Risperidone Long-Acting Intramuscular Injection in Subjects With Schizophrenia</a></li><li><a href="/node/182">NCT00604279 - R092670PSY3008 - A Randomized, Open-Label, Parallel Group Comparative Study of Paliperidone Palmitate (50, 100, 150 mg eq) and Risperidone LAI (25, 37.5, or 50 mg) in Subjects with Schizophrenia</a></li><li><a href="/node/190">NCT00590577 - R092670PSY3007 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Dose Response Study to Evaluate the Efficacy and Safety of 3 Fixed Doses (25 mg eq., 100 mg eq., and 150 mg eq.) of Paliperidone Palmitate in Subjects With Schizophrenia</a></li><li><a href="/node/191">NCT00111189 - R092670PSY3001 - A Randomized Double-blind Placebo-controlled Parallel Group Study Evaluating Paliperidone Palmitate in the Prevention of Recurrence in Patients With Schizophrenia. Placebo Consists of 20% Intralipid (200 mg/mL) Injectable Emulsion</a></li><li><a href="/node/192">NCT00210717 - R092670PSY3002 - A Randomized, Double-Blind, Parallel Group, Comparative Study of Flexibly Dosed Paliperidone Palmitate (25, 50, 75, or 100 mg eq.) Administered Every 4 Weeks and Flexibly Dosed RISPERDAL CONSTA (25, 37.5, or 50 mg) Administered Every 2 Weeks in Subjects With Schizophrenia</a></li><li><a href="/node/193">NCT00119756 - R092670PSY3005 - A Randomized, Crossover Study to Evaluate the Overall Safety and Tolerability of Paliperidone Palmitate Injected in the Deltoid or Gluteus Muscle in Patients With Schizophrenia</a></li><li><a href="/node/194">NCT00210548 - R092670PSY3003 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Dose-Response Study to Evaluate the Efficacy and Safety of 3 Fixed Doses (50 mg eq., 100 mg eq., and 150 mg eq.) of Paliperidone Palmitate in Subjects With Schizophrenia</a></li><li><a href="/node/195">NCT00101634 - R092670PSY3004 - A Randomized, Double-blind, Placebo-controlled, Parallel-group, Dose-response Study to Evaluate the Efficacy and Safety of 3 Fixed Doses (25 mg eq, 50 mg eq, and 100 mg eq) of Paliperidone Palmitate in Patients With Schizophrenia</a></li><li><a href="/node/196">NCT00391222 - RISBMN3001 - A Randomized, Double Blind, Placebo and Active Controlled Parallel Group Study to Evaluate the Efficacy and Safety of Risperidone Long-acting Injectable (LAI) for the Prevention of Mood Episodes in the Treatment of Subjects With Bipolar I Disorder</a></li><li><a href="/node/197">NCT00034749 - RIS-USA-231 - The Efficacy and Safety of Risperidone in Adolescents With Schizophrenia: a Comparison of Two Dose Ranges of Risperidone</a></li><li><a href="/node/198">NCT00076115 - RIS-BIM-301 - Research on the Effectiveness of Risperidone in Bipolar Disorder in Adolescents and Children (REACH): A Double-Blind, Randomized, Placebo-Controlled Study of the Efficacy and Safety of Risperidone for the Treatment of Acute Mania in Bipolar I Disorder</a></li><li><a href="/node/199">NCT00132678 - RISBIM3003 - A Randomized, Double-blind, Placebo-controlled Study to Explore the Efficacy and Safety of Risperidone Long-acting Intramuscular Injectable in the Prevention of Mood Episodes in Bipolar 1 Disorder, With Open-label Extension</a></li><li><a href="/node/200">NCT00094926 - RIS-BIP-302 - A Prospective, Randomized, Double-blind, Placebo-controlled Study of the Effectiveness and Safety of RISPERDAL CONSTA Augmentation in Adult Patients With Frequently-relapsing Bipolar Disorder</a></li><li><a href="/node/296">NCT00397033 - R076477SCA3001 - A Randomized, Double-blind, Placebo-controlled, Parallel-group Study to Evaluate the Efficacy and Safety of Two Dosages of Paliperidone ER in the Treatment of Patients With Schizoaffective Disorder</a></li><li><a href="/node/297">NCT00412373 - R076477SCA3002 - A Randomized, Double-blind, Placebo-controlled, Parallel- Group Study to Evaluate the Efficacy and Safety of Flexible-dose Paliperidone ER in the Treatment of Patients With Schizoaffective Disorder</a></li><li><a href="/node/298">NCT00236444 - CR002020 (RIS-INT-79) - Risperidone in the Prevention of Relapse: a Randomized, Double-blind, Placebo-controlled Trial in Children and Adolescents With Conduct and Other Disruptive Behavior Disorders</a></li><li><a href="/node/299">NCT00236470 - CR002149 (RIS-INT-84) - Risperidone in the Treatment of Children and Adolescents With Conduct and Other Disruptive Behavior Disorders - an Open Label Follow-up Trial of CR002020</a></li><li><a href="/node/300">NCT00250354 - CR006007 (RIS-CAN-19) - The Safety And Efficacy Of Risperidone Versus Placebo In Conduct Disorder In Mild, Moderate And Borderline Mentally Retarded Children Aged 5 To 12 Years</a></li><li><a href="/node/301">NCT00266552 - CR006019 (RIS-USA-93) - The Safety And Efficacy Of Risperidone Versus Placebo In Conduct Disorder and Other Disruptive Behavior Disorders In Mild, Moderate And Borderline Mentally Retarded Children Aged 5 To 12 Years</a></li><li><a href="/node/495">Multiple - OPTICS Trial Bundle</a></li><li><a href="/node/548">NCT00249132 - RIS-INT-3 - A Canadian multicenter placebo-controlled study of fixed doses of risperidone and haloperidol in the treatment of chronic schizophrenic patients</a></li><li><a href="/node/562">NCT00216476 - RISSCH3001 - CONSTATRE: Risperdal® Consta® Trial of Relapse Prevention and Effectiveness</a></li><li><a href="/node/563">NCT00216580 - RIS-PSY-301 - An Open-label Trial of Risperidone Long-acting Injectable in the Treatment of Subjects With Recent Onset Psychosis</a></li><li><a href="/node/576">NCT00253162 - RIS-INT-69 - The Efficacy And Safety Of Flexible Dose Ranges Of Risperidone Versus Placebo Or Haloperidol In The Treatment Of Manic Episodes Associated With Bipolar I Disorder</a></li><li><a href="/node/589">NCT00378092 - CR011992, RISSCH3024 - A Prospective Study of the Clinical Outcome Following Treatment Discontinuation After Remission in First-Episode Schizophrenia</a></li><li><a href="/node/622">NCT00299715 - R076477-BIM-3001 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Dose-Response, Multicenter Study to Evaluate the Efficacy and Safety of Three Fixed Doses of Extended-Release Paliperidone in the Treatment of Subjects With Acute Manic and Mixed Episodes Associated With Bipolar I Disorder</a></li><li><a href="/node/623">NCT00309699 - R076477-BIM-3002 - A Randomized, Double-Blind, Active- and Placebo-Controlled, Parallel-Group, Multicenter Study to Evaluate the Efficacy and Safety of Flexibly-Dosed, Extended-Release Paliperidone Compared With Flexibly-Dosed Quetiapine and Placebo in the Treatment of Acute Manic and Mixed Episodes Associated With Bipolar I Disorder</a></li><li><a href="/node/624">NCT00309686 - R076477-BIM-3003 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Multicenter Study to Evaluate the Efficacy and Safety of Flexibly-Dosed Extended-Release Paliperidone as Adjunctive Therapy to Mood Stabilizers in the Treatment of Acute Manic and Mixed Episodes Associated With Bipolar I Disorder</a></li><li><a href="/node/625">NCT00752427 - R076477-SCH-702 - 24 week extension of NCT00085748: A Randomized, 6-Week Double-Blind, Placebo-Controlled Study With an Optional 24-Week Open-Label Extension to Evaluate the Safety and Tolerability of Flexible Doses of Paliperidone Extended Release in the Treatment of Geriatric Patients With Schizophrenia</a></li><li><a href="/node/626">NCT00077714 - R076477-SCH-304 - A Randomized, Double-blind, Placebo- and Active-controlled, Parallel-group, Dose-response Study to Evaluate the Efficacy and Safety of 2 Fixed Dosages of Paliperidone Extended Release Tablets and Olanzapine, With Open-label Extension, in the Treatment of Patients With Schizophrenia</a></li><li><a href="/node/627">NCT00083668 - R076477-SCH-305 - A Randomized, Double-blind, Placebo- and Active-controlled, Parallel-group, Dose-response Study to Evaluate the Efficacy and Safety of 3 Fixed Dosages of Paliperidone Extended Release (ER) Tablets and Olanzapine, With Open-label Extension, in the Treatment of Patients With Schizophrenia</a></li><li><a href="/node/628">NCT00074477 - R092670-SCH-201 - A Randomized, Double-Blind, Placebo-Controlled Study to Evaluate the Efficacy and Safety of 50 and 100 Mg-eq of Paliperidone Palmitate in Patients With Schizophrenia</a></li><li><a href="/node/638">NCT00078039 - R076477-SCH-303 - Trial Evaluating Three Fixed Dosages of Paliperidone Extended-Release (ER) Tablets and Olanzapine in the Treatment of Patients With Schizophrenia</a></li><li><a href="/node/704">NCT00085748 - R076477-SCH-302 - A Randomized, 6-Week Double-Blind, Placebo-Controlled Study With an Optional 24-Week Open-Label Extension to Evaluate the Safety and Tolerability of Flexible Doses of Paliperidone Extended Release in the Treatment of Geriatric Patients With Schizophrenia</a></li><li><a href="/node/852">NCT00261508 - RIS-CAN-23/CR006106 - Efficacy And Safety Of Risperidone In The Treatment Of Children With Autistic Disorder And Other Pervasive Developmental Disorders: A Canadian, Multicenter, Double-Blind, Placebo-Controlled Study</a></li><li><a href="/node/853">NCT00249236 - RIS-IND-2/CR006064 - The Efficacy And Safety Of Flexible Dosage Ranges Of Risperidone Versus Placebo In The Treatment Of Manic Or Mixed Episodes Associated With Bipolar I Disorder</a></li><li><a href="/node/855">NCT00250367 - RIS-INT-46/CR006058 - The Safety And Efficacy Of Risperdal (Risperidone) Versus Placebo As Add-On Therapy To Mood Stabilizers In The Treatment Of The Manic Phase Of Bipolar Disorder</a></li><li><a href="/node/857">NCT00088075 - RIS-SCH-302/CR003370 - A Randomized, Double-Blind, Placebo-Controlled Clinical Study of the Efficacy and Safety of Risperidone for the Treatment of Schizophrenia in Adolescents</a></li><li><a href="/node/858">RIS-USA-1 (RIS-USA-9001) - Risperidone versus haloperidol versus placebo in the treatment of schizophrenia</a></li><li><a href="/node/859">NCT00253149 - RIS-USA-102/CR006040 - The Safety And Efficacy Of Risperdal (Risperidone) Versus Placebo Versus Haloperidol As Add-On Therapy To Mood Stabilizers In The Treatment Of The Manic Phase Of Bipolar Disorder</a></li><li><a href="/node/860">NCT00253136 - RIS-USA-121/CR006055 - Risperidone Depot (Microspheres) vs. Placebo in the Treatment of Subjects With Schizophrenia</a></li><li><a href="/node/861">RIS-USA-150 - A double-blind, placebo-controlled study of risperidone in children and adolescents with autistic disorder</a></li><li><a href="/node/863">NCT00257075 - RIS-USA-239/CR006052 - The Efficacy And Safety Of Flexible Dosage Ranges Of Risperidone Versus Placebo In The Treatment Of Manic Episodes Associated With Bipolar I Disorder</a></li><li><a href="/node/864">RIS-USA-240 - The efficacy and safety of flexible dose ranges of risperidone vs. Placebo or divalproex sodium in the treatment of manic or mixed episodes associated with bipolar 1 disorder</a></li><li><a href="/node/866">RIS-USA-72 - The safety and efficacy of risperidone 8 mg qd and 4 mg qd compared to placebo in the treatment of schizophrenia</a></li><li><a href="/node/867">NCT01529515 - R092670PSY3012  - A Randomized, Multicenter, Double-Blind, Relapse Prevention Study of Paliperidone Palmitate 3 Month Formulation for the Treatment of Subjects With Schizophrenia</a></li><li><a href="/node/868">NCT01193153 - R092670SCA3004 - A Randomized, Double-Blind, Placebo-Controlled, Parellel-Group Study of Paliperidone Palmitate Evaluating Time to Relapse in Subjects With Schizoaffective Disorder </a></li><li><a href="/node/869">NCT01662310 - R076477-SCH-3041 - Paliperidone Extended Release Tablets for the Prevention of Relapse in Subjects With Schizophrenia: A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group Study</a></li><li><a href="/node/870">NCT00490971 - R076477BIM3004 - A Randomized, Double-Blind, Active- and Placebo-controlled, Parallel-group, Multicenter Study to Evaluate the Efficacy and Safety of Extended-Release Paliperidone as Maintenance Treatment After an Acute Manic or Mixed Episode Associated With Bipolar I Disorder</a></li><li><a href="/node/871">NCT00524043 - R076477SCH4012 - A Randomized, Double-Blind, Placebo- and Active-Controlled, Parallel-Group Study to Evaluate the Efficacy and Safety of a Fixed Dosage of 1.5 mg/Day of Paliperidone Extended Release (ER) in the Treatment of Subjects With Schizophrenia</a></li><li><a href="/node/872">NCT00105326 - R076477-SCH-1010/CR002281 - A Double-blind, Placebo-controlled, Randomized Study Evaluating the Effect of Paliperidone ER Compared With Placebo on Sleep Architecture in Subjects With Schizophrenia</a></li><li><a href="/node/1032">NCT00645307 - R076477-SCH-701 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group Study With an Open-Label Extension Evaluating Extended Release OROS® Paliperidone in the Prevention of Recurrence in Subjects With Schizophrenia - Open Label Phase</a></li><li><a href="/node/1116">NCT00246246 - RIS-BIP-301 - A Randomized, Open-label Trial of RISPERDAL® CONSTA™ Versus Oral Antipsychotic Care in Subjects With Bipolar Disorder</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2018

2017-1816

Project Title: 
MASTERMIND: Stratification of response to SGLT2 inhibitor glucose lowering therapy
Specific Aims of the Project: 

The aim of this research is to identify clinical characteristics and routinely measured biomarkers that predict treatment response and side effects for SGLT2 inhibitors (SGLT2I) relative to alternative therapies. The ultimate aim is to help doctors treat patients with Type 2 diabetes with the drug most likely to work well for them.

Objectives
Our objectives are to:
1.Identify if kidney function is associated with glucose lowering response to SGLT2I treatment
2.Identify whether clinical characteristics and blood tests associated with insulin secretion and insulin resistance are associated with glucose lowering response
3.Determine whether patients with higher glucose, and better glucose lowering response, have more side effects
4.Explore what other characteristics might help predict glucose lowering and side effects with SGLT2I

We will test two specific hypotheses:
A. That participants with high baseline glycaemia will have a higher incidence of glycosuria related side effects and treatment discontinuation with SGLT2 in comparison to placebo and comparator therapies at the same level of baseline glycaemia.
B. That glycosuria related side effects will be more common in those with increased glucose lowering response at a given level of baseline glycaemia

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
Current guidelines for treating patients with Type 2 diabetes list a large number of drugs without giving clear guidance on which patients should have which drug. This makes it difficult for patients and their health care professionals to know which drugs are likely to suit them best. We know that patients with Type 2 diabetes vary greatly in how well they respond to different diabetes drugs, and whether they develop side effects.

Objective
To identify clinical characteristics associated with treatment response and side effects for SGLT2 inhibitor (SGLT2I) glucose lowering therapy.

Study design
A cohort study assessing the relationship between participant baseline characteristics and treatment response/side effects in those randomised to and receiving Canagliflozin therapy verses placebo, DPP4 inhibitor or sulfonylurea comparator. Where possible we will pool data from these studies at an individual level.

Participants
Individual patient level data from participants receiving SGLTI or comparator therapy (n>5400).

Main Outcome Measure
Change in HbA1c at 26 weeks.

Statistical analysis
We will examine clinical predictors of response to SGLT2I (HbA1c change). We will assess whether factors associated with glycaemic response to SGLT2I are also associated with response to comparator treatments, and with pre-specified side effects. Findings will be cross validated in additional trial and electronic healthcare record data sets available to the MASTERMIND consortium.

Brief Project Background and Statement of Project Significance: 

This research forms part of a larger project funded by the UK Medical Research Council (MASTERMIND) studying stratification of glucose lowering treatment in Type 2 diabetes. Our vision is that a stratified medicine approach based on routinely available clinical characteristics and biomarkers will result in more effective use of glucose- lowering therapy for patients with Type 2 diabetes

There are a large and increasing number of glucose lowering therapies available for Type 2 diabetes with no clear rationale given for choice of one over another in current clinical guidelines beyond side effect profile and cost(1,2). The mechanism of action of glucose lowering therapies varies widely, with SGLT2 inhibitors (SGLT2I), a commonly used second and third line treatment class, acting through potentiate of renal glucose loss, in contrast to other common non-insulin therapies whose mechanisms of action include potentiation of insulin secretion, increasing insulin sensitivity, suppression of glucagon or effects on glucose absorption (3).

Patients with Type 2 diabetes show considerable inter-individual variation in both their underlying pathophysiology, and in their response to treatment (4, 5). There is increasing evidence that this variation in the response to therapy is, in part, robustly explained by differences in patients’ underlying pathophysiology (5-7). Identifying robust predictors of response to glucose lowering therapy, or to important side effects, may allow a stratified (or precision medicine) approach to therapy, where likely effectiveness or side effect risk is used to inform treatment choice.

Type 2 diabetes is common (>4% of the population) and most prescribing of relatively inexpensive therapy is in primary care. Therefore, for a stratified approach to be widely implemented it should ideally be based on clinical characteristics and readily available biomarkers; sophisticated and expensive testing, as used in conditions like cancer, is unlikely to be feasible (5).

The information gained from this work will be combined with results from large electronic healthcare record, cohort study and intervention trial data sets and an ongoing intervention crossover trial (see analysis plan), to produce robust evidence to inform guidelines for the most appropriate use of glucose lowering medication for specific subgroups of patients with Type 2 diabetes.

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

Studies that have been selected are randomised controlled trials of SGLT2 inhibitor therapy in adult participants with non-insulin treated Type 2 diabetes and baseline HbA1c >7% (53mmol/mol). All selected studies have assessed HbA1c change over >=26 weeks, have an active (DPP4 inhibitor, sulfonylurea) or placebo comparator and cohort size >=400.

Data analysis will be of the per protocol population.

Narrative Summary: 

The purpose of this research is to identify characteristics (such as weight or blood results) that predict treatment response and side effects for glucose lowering treatments, and ultimately help doctors treat patients with Type 2 diabetes with the drug most likely to work well for them. We will examine whether differences between people in studies of glucose lowering treatment studies (for example their age, weight, or common blood test results) can be used to identify those who are likely to have a large reduction in blood glucose and/or few side effects. We will compare results across many different studies and medications to ensure our results are true and accurate.

Project Timeline: 

Analysts for this research are already in post and working with data made available through other requests and therefore analysis can commence rapidly on data availability. Assuming 3 months to data availability we anticipate the following timeline:
October 2017 - commence analysis
April 2018 - complete analysis
August 2018 - submit manuscript and report results to the YODA project.

Dissemination Plan: 

Central to the communication of the research will be the dissemination to academic and scientific users of research both in
academia, charities and industry. We will do this via presentations at national (Diabetes UK) and international (EASD, ADA) conferences and open access publications in leading peer review journals (e.g. Lancet).

We will also directly engage with the industrial partners involved in MASTERMIND via our already established Industry Advisory Committee. We will also work with the UK Precision Medicine Catapult as it begins to be implemented in order to communicate our outputs to the broader industry community that do not have direct involvement with the project.

Communication to physicians and non academic clinicians is also crucial to maximise the reach and impact of the work. To ensure our findings are communicated to the wider medical community we will present our findings at locally and nationally at meetings attended by non academic clinicians, in the professional press and educational events (such as the training courses run by the Oxford and Exeter teams) . We will submit any validated stratification criteria to guideline providers to inform future treatment guidance, which will ensure wider take-up into clinical practice.

Bibliography: 

1. Inzucchi SE, Bergenstal RM, Buse JB, Diamant M, Ferrannini E, Nauck M, et al. Management of hyperglycemia in type 2 diabetes, 2015: a patient-centered approach: update to a position statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes care. 2015;38(1):140-9.
2. National Institute for Health and Care Excellence (2015) Type 2 diabetes in adults: management. NICE guideline (NG28).
3. American Diabetes Association. 8. Pharmacologic Approaches to Glycemic Treatment. Diabetes care. 2017;40(Suppl 1):S64-S74.
4. Tuomi T et al. The many faces of diabetes: a disease with increasing heterogeneity.. Lancet 2014 Mar 22;383(9922)
5. Hattersley A, Patel K. Precision diabetes: learning from monogenic diabetes. Diabetologia 2017 May;60(5):769-777
6. Pearson ER. Personalized medicine in diabetes: the role of 'omics' and biomarkers. Diabetes Medicine 2016 Jun;33(6):712-7
7. Jones AG at al. Markers of β-Cell Failure Predict Poor Glycemic Response to GLP-1 Receptor Agonist Therapy in Type 2 Diabetes. Diabetes Care 2016 Feb;39(2):250-7

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
Other
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

The primary outcome measure will be baseline adjusted (least squares) change in HbA1c at 26 weeks after commencement of study medication.

Secondary outcome measures will include:
1. Time to glycaemic failure defined by HbA1c >baseline HbA1c on two consecutive measurements >8 weeks apart or a single measurement >baseline with addition of ‘rescue’ therapy
2. Baseline adjusted HbA1c change at 52 and 104 weeks
3. The development of short term side effects known to be associated with SGLT2 therapy using trial definitions: Urinary tract infection, genital infection, hypoglycaemia, event consistent with volume depletion, polyuria, acute renal failure
4. Premature medication discontinuation due to an adverse event
5. Change in weight, blood pressure, eGFR (MDRD) and haematocrit at 24, 52 and 104 weeks

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

We will assess the relationships between glycaemic response and the following baseline characteristics, where available.
A. Estimated glomerular filtration rate (MDRD equation)
B. Glycaemia: baseline HbA1c, fasting glucose
C. Markers of beta cell failure: Diabetes duration, age of diagnosis, C-peptide (and/or insulin), insulogenic index, islet autoantibodies, proinsulin insulin ratio, HOMA2B
D. Markers of insulin resistance: BMI, fasting triglycerides, HDL, SHBG, HOMA2IR
Model fit will be assessed, and variables transformed or categorised where necessary, if model assumptions are not met.
Potential predictors may be grouped to create composite variables (e.g. does response differ in individuals exhibiting multiple characteristics associated with insulin resistance?).

Statistical Analysis Plan: 

1. Clinical predictors of glycaemic response:
i. Models of glycaemic response to SGLT2 therapy: We will examine clinical predictors of response (HbA1c change) within the first 24 weeks of therapy as a continuous measure using linear regression analysis, with baseline adjusted change in HbA1c as the outcome and clinical characteristics as the independent variables. Analysis will be adjusted for potential confounders including dose, study & co-therapy. This work will be extended further using more complex analysis taking into account placebo response (Royston Stat Med 2004 PMID 15287081, Wang Stat Med 2015 PMID 25736915). Analysis will be per protocol and restricted to participants with >80% adherence and no change in glucose lowering co-therapy at the time point of interest.
ii. Are characteristics associated with response specific to SGLT2I? To explore whether a characteristic is specifically associated with response to SGLT2I (rather than being associated with response to any treatment) we will assess the relationship between characteristics associated with SGLT2I response and response to DPP4i and Sulfonylurea therapy, using the same methods described in i. above.
iii. Exploration of confounding: The distribution of baseline characteristics will depend on the study of origin, which could confound results if variation in characteristics potentially predictive of response are not sufficiently represented in those treated with a particular agent. To ensure this is not confounding results we will explore the relationship between characteristics associated with response against placebo in the whole group (pooled results) and response within the individual studies.
iv. Validation of findings: It will be important to validate findings in other data sets. We have current access to trial data of >15000 response episodes through data requests managed by clinicalstudydatarequest.com (GSK, Boehringer Ingelheim and Takeda) and observational primary care response data for >2500000 patients with type 2 diabetes from the UK clinical practice research datalink (CPRD) and GoDARTS, which will provide data for replication. In addition we are undertaking a randomised double blind crossover study directly comparing SGLT2i, DPPIV and Pioglitazone therapy to test stratification hypotheses derived from other trial data (n=600), this will allow us to replicate findings in the setting of comparative within individual response against other treatments.
2. Side effects
i. Analysis: We will assess the relationship between any baseline characteristics associated with SGLT2 inhibitor response as a continuous variable and incidence of specific side effects above using survival based methods, such as cox regression, with adjustment for (depending on outcome of interest) age, gender, duration of diabetes, renal function, baseline glycaemia or liver function, study allocation, dose and co-therapy. We will explore the use of more complex modelling based on fractional polynomials taking into account occurrence of these events in the comparison groups (Roystan, Stat Med 2004, PMID 15287081).
ii. Confounding: the covariates of interest above may simply be prognostic factors of occurrence in the population, and unrelated to treatment allocation, rather than predictors of occurrence with SGLT2 treatment. The analysis in i above is therefore exploratory and methods adjusting for occurrence of these in a comparison group (such as that described above) will therefore be required to validate any findings from logistic regression.
iii. Validation of findings: as outlined above findings will be validated in the additional datasets available to the MASTERMIND consortium.
3. Precision estimate
Based on data from the GoDarts study and 3000 participants allocated to SGLT2I being eligible for analysis inclusion conventional regression analyses will have 90% power to detect a co-variate that explains <1% of variance in HbA1c reduction with an alpha <0.05.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><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/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/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></ol>
Make Publicly Available : 
Year of Data Access: 
2017

2017-1701

Project Title: 
Response to Placebo Treatment and Non-response to Active Drug Treatment in Clinical Trials of Long-Acting Injectable Antipsychotics for Schizophrenia
Specific Aims of the Project: 

The aims of this analysis are five-fold: (1) to compare symptom trajectories between placebo and active drug (i.e. LAI risperidone/paliperidone) responders; (2) to identify demographic and clinical characteristics associated with placebo response or occurrence of side effects in patients with schizophrenia who were receiving placebo injection; (3) to examine whether early placebo improvement at week 1 or 2 will be associated with placebo response at the endpoint, in order to guide systematic screening of potential placebo responders; (4) to compare blood concentrations of risperidone/palipridone between responders and non-responders; and (5) to explore a threshold of blood risperidone/pariperidone concentration below which a chance of response significantly increases.

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

Application Status

Complete
Scientific Abstract: 

Background: Poor adherence to study medications in clinical trials obscures interpretation of placebo response. Placebo-controlled trials of long-acting injectable (LAI) antipsychotics provide an ideal dataset to investigate placebo effects. On the other hand, despite the assured drug delivery, lack of adequate improvement with LAI antipsychotics is often observed.
Objective: Objectives are to examine demographic and clinical characteristics associated with placebo response or occurrence of side effects in patients with schizophrenia receiving placebo injection and to compare blood drug concentrations between responders and non-responders.
Study Design: A post-hoc analysis of placebo-controlled double-blind trial data.
Participants: Data from participants in the following studies will be used: NCT00101634, NCT00111189, NCT00210548, NCT00253136, NCT00590577, and NCT00074477.
Main Outcome Measures: Positive and Negative Syndrome Scale scores.
Statistical Analysis: First, placebo responders will be categorized into subtypes according to their symptom trajectories. Second, demographic and clinical characteristics of subjects who showed response or side effects with placebo treatment will be characterized. Third, blood concentrations of risperidone/palipridone will be compared between responders and non-responders. Finally, a threshold of blood risperidone/pariperidone concentration below which a chance of response significantly increases will be explored.

Brief Project Background and Statement of Project Significance: 

Mechanisms underlying placebo response are multifactorial and complex; psychological, methodological, and administrative factors are expected to be involved in this phenomenon. Among them, poor adherence to study medications in clinical trials obscures interpretation of placebo response. In this respect, long-acting injectable (LAI) antipsychotics provide a reliable drug delivery to patients whose adherence with oral medication is suboptimal (McEvoy, 2006; Patel et al., 2009). Therefore, placebo-controlled double-blind trials of LAI antipsychotics are expected to provide an ideal dataset to shed further light on placebo effects and placebo-active drug differentials.
Previous studies have focused on certain demographic and clinical characteristics in association with greater placebo response in patients with schizophrenia. For example, male gender and older age are reportedly associated with greater placebo effects in previous clinical trials for schizophrenia (Alphs et al., 2012). It should be noted that these findings are based on the results of clinical trials of oral antipsychotic drugs; the nature and degree of placebo effects may differ among drug formulations. Analysis of clinical trial data of patients with schizophrenia who showed response with placebo injection will allow us to explore demographic and clinical characteristics associated with placebo effects in this population.
On the other hand, despite the assured drug delivery, lack of adequate improvement with LAI antipsychotics is often observed (Hough et al., 2010; Kramer et al., 2010). As such, characterization of LAI nonresponsive patients, including demographic and clinical characteristics and pharmacokinetic profile, will improve our understanding of treatment resistance to a continuous dopamine blockade in schizophrenia. Especially if there is unique pharmacokinetic profile in such difficult-to-treat patients, the results will be utilized to provide individually tailored better treatment for them (e.g. further dose titration).
We therefore propose a post-hoc analysis of placebo-controlled double-blind trial data of LAI antipsychotics in order to provide evidence to characterize subjects with schizophrenia who showed clinical response with placebo injection and those who failed to show response despite LAI paliperidone/risperidone treatment. Associations between blood concentrations of risperidone/paliperidone and clinical effects will also be explored.
These results will be expected to provide critical insights in the design of future clinical trials in patients with schizophrenia so as to reduce failure of placebo-controlled antipsychotic clinical trials in patients with schizophrenia, and to be utilized for individually tailored treatment for such difficult-to-treat patients.

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

Datasets of the following studies will be used: NCT00101634, NCT00111189, NCT00210548, NCT00253136, NCT00590577, and NCT00074477.

Narrative Summary: 

Poor adherence to study medications in clinical trials obscures interpretation of placebo response. Placebo-controlled trials of long-acting injectable (LAI) antipsychotics that secure a drug delivery to patients provide an ideal dataset to investigate placebo effects. On the other hand, despite the assured drug delivery, lack of adequate improvement with LAI antipsychotics is often observed. We will characterize subjects with schizophrenia who showed response with placebo injection and those who failed to show response despite LAI treatment. These results will be expected to provide critical insights in the design of future clinical trials and utilized for individually tailored treatment.

Project Timeline: 

The anticipated project start date is the 1st of July, and analysis completion date will be the 31st of August. A manuscript will be drafted by the 31th of October, and it will be submitted for publication by the 31st of December. Results will be reported back to the YODA Project by the 30th of April.

Dissemination Plan: 

The manuscript will be submitted to academic journals whose target audiences include psychiatrists, pharmacologists, and general practitioners such as American Journal of Psychiatry, British Journal of Psychiatry, and Journal of Clinical Psychiatry.

Bibliography: 

Alphs, L., Benedetti, F., Fleischhacker, W.W., Kane, J.M., 2012. Placebo-related effects in clinical trials in schizophrenia: what is driving this phenomenon and what can be done to minimize it? Int J Neuropsychopharmacol 15(7), 1003-1014.
Hough, D., Gopal, S., Vijapurkar, U., Lim, P., Morozova, M., Eerdekens, M., 2010. Paliperidone palmitate maintenance treatment in delaying the time-to-relapse in patients with schizophrenia: a randomized, double-blind, placebo-controlled study. Schizophr Res 116(2-3), 107-117.
Kramer, M., Litman, R., Hough, D., Lane, R., Lim, P., Liu, Y., Eerdekens, M., 2010. Paliperidone palmitate, a potential long-acting treatment for patients with schizophrenia. Results of a randomized, double-blind, placebo-controlled efficacy and safety study. Int J Neuropsychopharmacol 13(5), 635-647.
Marder, S.R., Davis, J.M., Chouinard, G., 1997. The effects of risperidone on the five dimensions of schizophrenia derived by factor analysis: combined results of the North American trials. J Clin Psychiatry 58(12), 538-546.
McEvoy, J.P., 2006. Risks versus benefits of different types of long-acting injectable antipsychotics. The Journal of clinical psychiatry 67 Suppl 5, 15-18.
Patel, M.X., Taylor, M., David, A.S., 2009. Antipsychotic long-acting injections: mind the gap. Br J Psychiatry Suppl 52, S1-4.

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

Positive and Negative Syndrome Scale (PANSS) scores. Response will be defined as a percentage score reduction of 25% or more at endpoint in the PANSS.

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

Age groups (e.g. <60 or >=60); sex; baseline PANSS positive, negative, and general psychopathology subscale scores, and PANSS Marder 5-Factor scores (Marder et al., 1997); score reductions in PANSS total scores from baseline to week 1; blood risperidone/paliperidone concentrations.

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

Years of education, ethnicity, duration of illness, and cognitive performance scores (when available)

Statistical Analysis Plan: 

First, scores of the PANSS at baseline and week 1 and thereafter will be extracted. Differences in the degree of change in the PANSS scores (i.e. PANSS positive, negative, and general psychopathology subscale scores, and PANSS Marder 5-Factor scores) (Marder et al., 1997) over time in placebo and active drug groups will be investigated using a mixed-effects model for repeated measure (MMRM), that contained treatment group (placebo or active drug) and week, and group-by-week interaction as factors. This analysis will be repeated solely for placebo and active drug responders to examine if their response patterns are similar or different as a group. In addition, those placebo and active drug responders will be categorized into subgroups according to their symptom trajectories by using latent class analysis. Second, rates of response (i.e. a percentage score reduction of 25% or more at endpoint in the PANSS) will be calculated for those on placebo and active drugs, and compared using chi-squared tests. Third, multiple logistic regression analysis will be performed to evaluate association between placebo response at endpoint or PANSS score or percentage reduction from baseline to endpoint, and demographic and clinical characteristics that include baseline PANSS scores, gender, age, races, years of educations, and PANSS score change from baseline to week 1 in those receiving placebo. This analysis will also be performed for side effects with their incidence rates of >5%. Fourth, if PANSS score change from baseline to week 1 is found to be associated with subsequent placebo response, the following analysis will be performed. The prediction performance of binary classification in early placebo improvement at week 1 or 2, to predict response at endpoint, will be examined. To this end, sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) of the consecutive cut-off points in increments of 5% between a 5% to 50% reduction in PANSS scores from baseline to week 1 or 2 will be calculated. To seek the optimal cut-off point, both the accuracy, defined as (True Positive + True Negative) / Total N, and area under the curve (AUC) of receiver operating characteristic (ROC) will be calculated. Fifth, blood concentrations of risperidone/palipridone will be compared between subjects who showed response and those who did not. This analysis will also be conducted regarding side effects with their incidence rates of >5%. Finally, a threshold of blood risperidone/pariperidone concentration below which a chance of response significantly increases will be explored, using chi-squared test.
Available case analysis will be performed. Statistical analyses will be performed using SAS (SAS Institute Inc., Cary, North Carolina). A p-value of <0.05 is considered to indicate statistical significance (two-tailed).

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><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/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/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/860">NCT00253136 - RIS-USA-121/CR006055 - Risperidone Depot (Microspheres) vs. Placebo in the Treatment of Subjects With Schizophrenia</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2017
Associated Data: 
Results

2017-1676

Project Title: 
Placebo Effects in Schizophrenia
Specific Aims of the Project: 

The aims of this analysis are three-fold: (1) to compare symptom trajectories between placebo and active drug responders in acute phase trials; (2) to identify demographic and clinical characteristics associated with placebo response or occurrence of side effects in patients with schizophrenia; and (3) to examine whether early placebo improvement at week 1 is associated with placebo response at the endpoint, in order to guide systematic screening of potential placebo responders.

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

Complete
Scientific Abstract: 

Background: Response to antipsychotics is often difficult to quantify, which in turn resulted in a number of failed trials in that drugs have not established superiority over placebo treatment. In light of an increasing number of failed antipsychotic trials, it is important to investigate placebo effects in this population in order to optimize the design of future clinical trials.
Objective: Objectives are three-fold: to compare symptom trajectories between placebo and active drug responders to identify demographic and clinical characteristics associated with placebo response or occurrence of side effects in patients with schizophrenia; and to examine whether early placebo improvement is associated with placebo response at the endpoint.
Study Design: A post-hoc analysis of placebo-controlled double-blind trial data.
Participants: Data from participants in the following studies will be used: NCT00077714, NCT00078039, NCT00083668, NCT00085748, NCT00088075, NCT00249132, NCT00334126, NCT00518323, NCT00524043, RIS-USA-1, and RIS-USA-72.
Main Outcome Measures: Positive and Negative Syndrome Scale scores.
Statistical Analysis: First, symptom trajectories between placebo and active drug responders will be compared. Placebo responders will be categorized into subtypes according to their symptom trajectories. Second, optimal criteria for screening of potential placebo responders in a placebo lead-in phase will be investigated. Third, demographic and clinical characteristics of subjects who showed response or side effects with placebo treatment will be characterized.

Brief Project Background and Statement of Project Significance: 

Response to psychotropics is often difficult to quantify, which in turn has contributed to a number of failed trials in that drugs have not established superiority over placebo treatment. This may be especially true for antipsychotic clinical trials as symptom improvement with placebo treatment has been increasing since 1960 (Rutherford et al., 2014). In light of an increasing number of failed trials for schizophrenia, it is critically important to investigate placebo effects in this population in order to improve our understanding of placebo effects as well as to optimize the design of future clinical trials to mitigate such challenge.
Recent clinical trials frequently adopt a lead-in phase, in which placebo is given to participants in an effort to exclude placebo responders. However, the criteria adopted for exclusion of such participants have been arbitrary (e.g. a more than 25% total score reduction in the Positive and Negative Syndrome Scale [PANSS] or Brief Psychiatric Rating Scale [BPRS]) (Downing et al., 2014; Hamilton et al., 1998) and empiric. Systematic investigation of the magnitude and timing of placebo response and occurrence of side effects in patients with schizophrenia and how it differs from reaction to an active drug treatment would offer us a unique opportunity to shed light on critical issues in clinical trials in schizophrenia.
Mechanisms underlying placebo response are multifactorial and complex; psychological, methodological, and administrative factors are expected to be involved in this phenomenon. Among them, previous studies have focused on certain demographic and clinical characteristics in association with greater placebo response in patients with schizophrenia. For example, male gender and older age are reportedly associated with greater placebo effects (Alphs et al., 2012). Analysis of patient-level data of placebo responders/non-responders from previous clinical trials will allow us to provide more detailed information on demographic and clinical characteristics associated with placebo response, such as individual symptom severity at baseline (e.g. less negative symptoms). Moreover, we could make use of early symptomatic trajectories to predict longer term outcome.
We therefore propose a post-hoc analysis of placebo-controlled double-blind trial data in order to provide evidence to guide systematic screening of potential placebo responders with schizophrenia. First, we will compare symptom trajectories between placebo and active drug responders in acute phase trials. Placebo responders will be categorized into subtypes according to their symptom trajectories. Second, optimal criteria for screening of potential placebo responders in a placebo lead-in phase will be investigated. Third, we will try to characterize demographic and clinical characteristics of subjects who showed response or side effects with placebo treatment, respectively.
These results will be expected to provide critical insights in the design of future clinical trials in patients with schizophrenia so as to reduce failure of placebo-controlled antipsychotic clinical trials in patients with schizophrenia.

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

Datasets of the following studies will be used: NCT00077714, NCT00078039, NCT00083668, NCT00085748, NCT00088075, NCT00249132, NCT00334126, NCT00518323, NCT00524043, RIS-USA-1, and RIS-USA-72.

Narrative Summary: 

Response to antipsychotics is often difficult to measure, which in turn has resulted in numerous failed trials in that drugs have not shown superiority over placebo. Therefore, it is critically important to investigate placebo effects in this population in order to optimize the design of future clinical trials to mitigate such challenge. To this end, we will combine placebo-controlled double-blind trial data to provide evidence to screen potential placebo responders with schizophrenia. These results will be expected to provide critical insights in the design of future clinical trials in patients with schizophrenia so as to reduce failure of placebo-controlled antipsychotic clinical trials.

Project Timeline: 

The anticipated project start date is the 1st of July, and analysis completion date will be the 31st of August. A manuscript will be drafted by the 31th of October, and it will be submitted for publication by the 31st of December. Results will be reported back to the YODA Project by the 30th of April.

Dissemination Plan: 

The manuscript will be submitted to academic journals whose target audiences include psychiatrists, pharmacologists, and general practitioners such as American Journal of Psychiatry, British Journal of Psychiatry, and Journal of Clinical Psychiatry.

Bibliography: 

Alphs, L., Benedetti, F., Fleischhacker, W.W., Kane, J.M., 2012. Placebo-related effects in clinical trials in schizophrenia: what is driving this phenomenon and what can be done to minimize it? Int J Neuropsychopharmacol 15(7), 1003-1014.
Downing, A.M., Kinon, B.J., Millen, B.A., Zhang, L., Liu, L., Morozova, M.A., Brenner, R., Rayle, T.J., Nisenbaum, L., Zhao, F., Gomez, J.C., 2014. A Double-Blind, Placebo-Controlled Comparator Study of LY2140023 monohydrate in patients with schizophrenia. BMC Psychiatry 14, 351.
Hamilton, S.H., Revicki, D.A., Genduso, L.A., Beasley, C.M., Jr., 1998. Olanzapine versus placebo and haloperidol: quality of life and efficacy results of the North American double-blind trial. Neuropsychopharmacology 18(1), 41-49.
Marder, S.R., Davis, J.M., Chouinard, G., 1997. The effects of risperidone on the five dimensions of schizophrenia derived by factor analysis: combined results of the North American trials. J Clin Psychiatry 58(12), 538-546.
Rutherford, B.R., Pott, E., Tandler, J.M., Wall, M.M., Roose, S.P., Lieberman, J.A., 2014. Placebo response in antipsychotic clinical trials: a meta-analysis. JAMA Psychiatry 71(12), 1409-1421.

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

Positive and Negative Syndrome Scale (PANSS) scores. Response will be defined as a percentage score reduction of 25% or more at endpoint in the PANSS.

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

Age groups (e.g. <60 or >=60); sex; baseline PANSS positive, negative, and general psychopathology subscale scores, and PANSS Marder 5-Factor scores (Marder et al., 1997); score reductions in PANSS total scores from baseline to week 1.

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

Years of education, ethnicity, duration of illness, and cognitive performance scores (when available).

Statistical Analysis Plan: 

First, scores of the PANSS at baseline and week 1 and thereafter will be extracted. Differences in the degree of change in the PANSS scores (i.e. PANSS positive, negative, and general psychopathology subscale scores, and PANSS Marder 5-Factor scores) (Marder et al., 1997) over time in placebo and active drug groups will be investigated using a mixed-effects model for repeated measure (MMRM), that contained treatment group (placebo or active drug) and week, and group-by-week interaction as factors. This analysis will be repeated solely for placebo and active drug responders to examine if their response patterns are similar or different as a group. In addition, those placebo and active drug responders will be categorized into subgroups according to their symptom trajectories by using latent class analysis. Second, rates of response (i.e. a percentage score reduction of 25% or more at endpoint in the PANSS) will be calculated for those on placebo and active drugs, and compared using chi-squared tests. Third, multiple logistic regression analysis will be performed to evaluate association between placebo response at endpoint or PANSS score or percentage reduction from baseline to endpoint, and demographic and clinical characteristics that include baseline PANSS scores, gender, age, races, years of educations, and PANSS score change from baseline to week 1 in those receiving placebo. This analysis will also be performed for side effects with the incidence rates of >5%. Fourth, if PANSS score change from baseline to week 1 is found to be associated with subsequent placebo response, the following analysis will be performed. The prediction performance of binary classification in early placebo improvement at week 1, to predict response at week 6, will be examined. To this end, sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) of the consecutive cut-off points in increments of 5% between a 5% to 50% reduction in PANSS scores from baseline to week 1 or 2 will be calculated. To seek the optimal cut-off point, both the accuracy, defined as (True Positive + True Negative) / Total N, and area under the curve (AUC) of receiver operating characteristic (ROC) will be calculated.
Available case analysis will be performed. Statistical analyses will be performed using SAS (SAS Institute Inc., Cary, North Carolina). A p-value of <0.05 is considered to indicate statistical significance (two-tailed).

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><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/548">NCT00249132 - RIS-INT-3 - A Canadian multicenter placebo-controlled study of fixed doses of risperidone and haloperidol in the treatment of chronic schizophrenic patients</a></li><li><a href="/node/626">NCT00077714 - R076477-SCH-304 - A Randomized, Double-blind, Placebo- and Active-controlled, Parallel-group, Dose-response Study to Evaluate the Efficacy and Safety of 2 Fixed Dosages of Paliperidone Extended Release Tablets and Olanzapine, With Open-label Extension, in the Treatment of Patients With Schizophrenia</a></li><li><a href="/node/627">NCT00083668 - R076477-SCH-305 - A Randomized, Double-blind, Placebo- and Active-controlled, Parallel-group, Dose-response Study to Evaluate the Efficacy and Safety of 3 Fixed Dosages of Paliperidone Extended Release (ER) Tablets and Olanzapine, With Open-label Extension, in the Treatment of Patients With Schizophrenia</a></li><li><a href="/node/638">NCT00078039 - R076477-SCH-303 - Trial Evaluating Three Fixed Dosages of Paliperidone Extended-Release (ER) Tablets and Olanzapine in the Treatment of Patients With Schizophrenia</a></li><li><a href="/node/704">NCT00085748 - R076477-SCH-302 - A Randomized, 6-Week Double-Blind, Placebo-Controlled Study With an Optional 24-Week Open-Label Extension to Evaluate the Safety and Tolerability of Flexible Doses of Paliperidone Extended Release in the Treatment of Geriatric Patients With Schizophrenia</a></li><li><a href="/node/857">NCT00088075 - RIS-SCH-302/CR003370 - A Randomized, Double-Blind, Placebo-Controlled Clinical Study of the Efficacy and Safety of Risperidone for the Treatment of Schizophrenia in Adolescents</a></li><li><a href="/node/858">RIS-USA-1 (RIS-USA-9001) - Risperidone versus haloperidol versus placebo in the treatment of schizophrenia</a></li><li><a href="/node/866">RIS-USA-72 - The safety and efficacy of risperidone 8 mg qd and 4 mg qd compared to placebo in the treatment of schizophrenia</a></li><li><a href="/node/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></ol>
Make Publicly Available : 
Year of Data Access: 
2017
Associated Data: 
Results

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