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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
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>

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): 
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>

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): 
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>

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): 
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 - 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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 - 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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): 
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>

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
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>

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): 
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>

2017-1671

Project Title: 
Comparative safety and effectiveness of cognitive enhancers for Alzheimer's dementia: a systematic review and IPD NMA
Specific Aims of the Project: 

The aim of this study is to examine the comparative effectiveness and safety of cognitive enhancers versus placebo or best supportive care by patient characteristics, such as AD severity and sex. We will use IPD-NMA to identify potential treatment effect modifiers, and estimate the most effective and safest treatments for patients with different characteristics. The outputs of our project are to provide clinicians, patients and caregivers with tailored evidence to inform their decision making, improving the health of patients living with AD.

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

Background: Alzheimer's dementia (AD) is the most common cause of dementia, and several organisations, such as the National Institute for Health and Care Excellence, suggest that management of patients with AD should be tailored to their needs. To date, little research has been conducted on the treatment effect in different subgroups of patients with AD.

Objective: To examine the comparative effectiveness and safety of cognitive enhancers for different patient characteristics.

Study design: Systematic review of randomised clinical trials of any duration comparing cognitive enhancers alone or in any combination against other cognitive enhancers, or placebo in adults with AD.

Participants: Adults (aged ≥18 years) diagnosed with AD

Main Outcome Measures: The primary outcome of interest is cognition according to the Mini-mental State Examination (MMSE), and the secondary outcome is overall serious adverse events.

Statistical Analysis: We will perform a Bayesian hierarchical random-effects meta-analysis combining the individual patient data (IPD) from each eligible study. If the identified treatment comparisons form a connected network diagram, we will perform an IPD network meta-analysis (NMA) to estimate subgroup effects for patients with different characteristics, such as AD severity and sex. We will combine aggregated data from studies that we will not be able to obtain IPD, with the IPD provided by the original authors, in a single model. We will use the PRISMA-IPD[1] and PRISMA-NMA[2] statements to report our findings.

Brief Project Background and Statement of Project Significance: 

Alzheimer's dementia (AD) is the most common cause of dementia, and has an insidious onset with progressive deterioration in cognition (eg, memory, thinking and perception), function, behaviour and mood. To date, 46.8 million people worldwide live with dementia. This number will almost double every 20 years, and it is estimated to reach 131.5 million by 2050.[3] A study showed that as age increases, the rates of AD increase overall for both men and women, but it is more prevalent in women (rate/100 years=2.50 (1.85–3.41)) than men (rate/100 years=1.89 (1.22–2.94)).[4] It is currently unclear if galantamine, rivastigmine or donepezil should be used by patients with severe AD, and whether memantine is the most optimal treatment for severe AD.[5] The use of acetylcholinesterase inhibitors and increased doses of donepezil in patients with dementia increase the risk of bradycardia, as well, cholinesterase inhibitors doubles the risk of hospitalisation for bradycardia in older patients.[6, 7] Also, the use of other medications may increase risk of adverse events. For example, cardiac medications like β-blockers may increase risk of bradycardia, and anti-inflammatories may increase risk for gastrointestinal bleeding.[6, 8-10]

To determine the relative effectiveness and safety of cognitive enhancers for patients with different patient characteristics (eg, mild-moderate AD vs severe AD, females vs males), we aim to conduct a systematic review and individual patient data (IPD) network meta-analysis (NMA). In AD, patients may respond differently to the medication based on severity of AD and sex, and hence severity and sex could be considered treatment effect modifiers. The optimal approach to tailor results to the patient characteristics is via using IPD. Tailoring the management of patients with AD is an issue that has been also brought up by several organisations,[11] including the Alzheimer's Society of Ontario[12] and the National Institute for Health and Care Excellence (NICE).[13] Also, the Alzheimer's Disease International (ADI) federation in their world Alzheimer report 2015 mention that there has been dramatically little research into the treatment effect across people of different age and sex.[3]

We previously attempted a systematic review and NMA of aggregated data, but we were unable to provide definitive conclusions regarding the influence of patient characteristics on the results.[14, 15] In this study we tailored results to age, AD severity, comorbidity and study duration via subgroup analysis. These results were similar to 4 Cochrane reviews examining cognitive enhancers for AD.[16-19] The reviews showed that donepezil, rivastigmine and galantamine, significantly improved cognition[16-19] against placebo, yet cholinisterase inhibitors overall and donepezil improved behaviour,[16, 17] cholinisterase inhibitors overall and rivastigmine improved function,[17, 18] and rivastigmine improved AD severity.[18] The use of IPD will increase power and will help explain the relationship between treatment effects and patient-level characteristics.

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

We have updated our previous review[15] using the following criteria:
• Population: Adults (aged ≥18 years) with AD diagnosed using various criteria (eg, Diagnostic and Statistical Manual of Mental Disorders, Nursing Minimum Data Set criteria) of any duration with either moderate AD.[20]
• Interventions: Cognitive enhancers (donepezil, rivastigmine, galantamine and memantine) alone or in any combination.
• Comparators: Cognitive enhancers, best supportive care alone or in any combination, and placebo.
• Outcomes: MMSE and overall serious adverse events.
• Study design: We will restrict to RCTs, and will exclude quasi-RCTs.
• Time: Studies of any duration conducted at any time.
• Other: Published studies written in any language will be included.
In case study publications reported data from the same study group (eg, companion reports), we included the most recent study.
Our systematic reviews identified 139 relevant studies. We will include IPD from the studies reported in the supplementary, as well as aggregate data from all remaining published studies.

Narrative Summary: 

Alzheimer’s Dementia (AD) is the most common cause of dementia. Patients living with AD have a lower quality of life (deterioration in memory, thinking, perception, function, behaviour, and mood) and AD ultimately leads to death. Currently, there is no cure for AD, and patients may respond differently to the medication based on their characteristics (eg, severity of disease, sex). We aim to investigate the association between the cognitive enhancers for different patient characteristics and Mini-mental State Examination or overall serious adverse events. The findings of this study will help to improve guidelines for the management of patients with AD.

Project Timeline: 

Our study protocol was published on 7 December 2015 in an open access journal (see http://bmjopen.bmj.com/content/6/1/e010251). We started contacting the study authors to request for their IPD on 10 June 2016. By the time we receive the IPD we will collect, review and clean the data within 3-4 months. We anticipate that the statistical analyses will take another 3-4 months, depending on the complexity of the models and data. We will need approximately 2-3 months to prepare the manuscript and submit it. We expect the first submission of the manuscript will be approximately in March 2018.

Dissemination Plan: 

The findings of our study will fill an important knowledge gap in healthcare, and will be used to inform decision-making for patients suffering from this debilitating disease. The results of this systematic review and IPD-NMA will be of interest to stakeholders, including decision makers, guideline developers, clinicians, methodologists and patients. The dissemination of our findings will be knowledge user-driven and tailored to how and when knowledge users want to receive information. Team members will act as knowledge brokers, using their networks to facilitate dissemination, such as The Cochrane Collaboration, PRISMA-IPD, Drug Safety and Effectiveness Network (DSEN). We will also host a knowledge exchange event with our partners to discuss the results and facilitate dissemination. We will publish our findings in an open access journal, and present them at relevant meetings (Canadian Geriatrics Society; CGS), as well to newsletters of organisations (Alzheimer's Society of Ontario, CGS).

Bibliography: 

1. Stewart LA, Clarke M, Rovers M, et al. Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data: the PRISMA-IPD Statement. JAMA. 2015;313(16):1657-65.
2. Hutton B, Salanti G, Caldwell DM, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med. 2015;162(11):777-84.
3. Prince M, Wimo A, Guerchet M, Ali GC, Wu YT, Prina M. World Alzheimer Report 2015. The global impact of dementia. An analysis of prevalence, incidence, cost and trends. London, UK; 2015.
4. Katz MJ, Lipton RB, Hall CB, et al. Age-specific and sex-specific prevalence and incidence of mild cognitive impairment, dementia, and Alzheimer dementia in blacks and whites: a report from the Einstein Aging Study. Alzheimer Dis Assoc Disord. 2012;26(4):335-43.
5. National Institute for Health and Clinical Excellence. Donepezil, galantamine, rivastigmine and memantine for the treatment of Alzheimer's disease. London, UK; 2011.
6. Park-Wyllie LY, Mamdani MM, Li P, Gill SS, Laupacis A, Juurlink DN. Cholinesterase inhibitors and hospitalization for bradycardia: a population-based study. PLoS Med. 2009;6(9):e1000157.
7. Hernandez RK, Farwell W, Cantor MD, Lawler EV. Cholinesterase inhibitors and incidence of bradycardia in patients with dementia in the veterans affairs new England healthcare system. J Am Geriatr Soc. 2009;57(11):1997-2003.
8. Manurung D, Trisnohadi HB. Beta blockers for congestive heart failure. PLoS Med. 2007;39(1):44-8.
9. Gheorghiade M, Colucci WS, Swedberg K. Beta-blockers in chronic heart failure. Circulation. 2003;107(12):1570-5.
10. Pahor M, Guralnik JM, Furberg CD, Carbonin P, Havlik R. Risk of gastrointestinal haemorrhage with calcium antagonists in hypertensive persons over 67 years old. Lancet. 1996;347(9008):1061-5.
11. Sindi S, Mangialasche F, Kivipelto M. Advances in the prevention of Alzheimer's Disease. F1000Prime Rep. 2015;7:50.
12. Williams AP, Peckham A, Rudoler D, Tam T, Watkins J. Formative evaluation of the Alzheimer society of Toronto counselling program. Alzheimer society of Toronto; 2013.
13. National Institute for Health and Clinical Excellence. Dementia: Supporting people with dementia and their carers in health and social care. London, UK; 2006.
14. Tricco AC, Vandervaart S, Soobiah C, et al. Efficacy of cognitive enhancers for Alzheimer's disease: protocol for a systematic review and network meta-analysis. Syst Rev. 2012;1:31.
15. Tricco AC, Ashoor HM, Rios P, et al. Comparative safety and effectiveness of cognitive enhancers for the treatment of Alzheimer’s disease: A rapidly updated systematic review and network meta-analysis Toronto, Canada; 2015.
16. Birks J, Harvey RJ. Donepezil for dementia due to Alzheimer's disease. Cochrane Database Syst Rev. 2006(1):Cd001190.
17. Birks J. Cholinesterase inhibitors for Alzheimer's disease. Cochrane Database Syst Rev. 2006(1):Cd005593.
18. Birks J, Grimley Evans J, Iakovidou V, Tsolaki M, Holt FE. Rivastigmine for Alzheimer's disease. Cochrane Database Syst Rev. 2009(2):Cd001191.
19. Loy C, Schneider L. Galantamine for Alzheimer's disease and mild cognitive impairment. Cochrane Database Syst Rev. 2006(1):Cd001747.
20. Burback D, Molnar FJ, St John P, Man-Son-Hing M. Key methodological features of randomized controlled trials of Alzheimer's disease therapy. Minimal clinically important difference, sample size and trial duration. Dement Geriatr Cogn Disord. 1999;10(6):534-40.
21. Cochrane Effective Practice and Organisation of Care Review Group (EPOC). Data Collection Checklist. Available from: http://methods.cochrane.org/sites/methods.cochrane.org.bias/files/public....
22. Higgins JPT, Green S (editors). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration, 2011. Available from www.handbook.cochrane.org.
23. Chaimani A, Higgins JP, Mavridis D, Spyridonos P, Salanti G. Graphical tools for network meta-analysis in STATA. PLoS One. 2013;8(10):e76654.
24. Puhan MA, Schunemann HJ, Murad MH, et al. A GRADE Working Group approach for rating the quality of treatment effect estimates from network meta-analysis. BMJ. 2014;349:g5630.
25. Turner RM, Omar RZ, Yang M, Goldstein H, Thompson SG. A multilevel model framework for meta-analysis of clinical trials with binary outcomes. Stat Med. 2000;19(24):3417-32.
26. Higgins JP, Whitehead A, Turner RM, Omar RZ, Thompson SG. Meta-analysis of continuous outcome data from individual patients. Stat Med. 2001;20(15):2219-41.
27. Sutton AJ, Kendrick D, Coupland CA. Meta-analysis of individual- and aggregate-level data. Stat Med. 2008;27(5):651-69.
28. Higgins JP, Jackson D, Barrett JK, Lu G, Ades AE, White IR. Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res Synth Methods. 2012;3(2):98-110.
29. White IR, Barrett JK, Jackson D, Higgins JP. Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression. Res Synth Methods. 2012;3(2):111-25.
30. Song F, Altman DG, Glenny AM, Deeks JJ. Validity of indirect comparison for estimating efficacy of competing interventions: empirical evidence from published meta-analyses. BMJ. 2003;326(7387):472.
31. Veroniki AA, Vasiliadis HS, Higgins JP, Salanti G. Evaluation of inconsistency in networks of interventions. Int J Epidemiol. 2013;42(1):332-45.
32. Donegan S, Williamson P, D'Alessandro U, Garner P, Smith CT. Combining individual patient data and aggregate data in mixed treatment comparison meta-analysis: Individual patient data may be beneficial if only for a subset of trials. Stat Med. 2013;32(6):914-30.
33. Donegan S, Williamson P, D'Alessandro U, Tudur Smith C. Assessing the consistency assumption by exploring treatment by covariate interactions in mixed treatment comparison meta-analysis: individual patient-level covariates versus aggregate trial-level covariates. Stat Med. 2012;31(29):3840-57.
34. Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc Series B Stat Methodol. 2002;64(4):583-639.
35. Salanti G, Ades AE, Ioannidis JP. Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J Clin Epidemiol. 2011;64(2):163-71.
36. Lambert PC, Sutton AJ, Burton PR, Abrams KR, Jones DR. How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS. Stat Med. 2005;24(15):2401-28.
37. Turner RM, Davey J, Clarke MJ, Thompson SG, Higgins JP. Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews. Int J Epidemiol. 2012;41(3):818-27.
38. Rhodes KM, Turner RM, Higgins JP. Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data. J Clin Epidemiol. 2015;68(1):52-60.
39. Mavridis D, White IR, Higgins JP, Cipriani A, Salanti G. Allowing for uncertainty due to missing continuous outcome data in pairwise and network meta-analysis. Stat Med. 2015;34(5):721-41.
40. Spineli LM, Higgins JP, Cipriani A, Leucht S, Salanti G. Evaluating the impact of imputations for missing participant outcome data in a network meta-analysis. Clin Trials. 2013;10(3):378-88.
41. Lunn D, Spiegelhalter D, Thomas A, Best N. The BUGS project: Evolution, critique and future directions. Stat Med. 2009;28(25):3049-67.

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
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 primary outcome of interest is cognition according to the MMSE (efficacy outcome, continuous variable), and the secondary outcome is overall serious adverse events (SAEs; safety outcome, dichotomous variable); both outcomes were reported by many of the included trials previously and for which NMA was possible. In particular, in our previous NMA using aggregated data, 60 RCTs with 15 862 patients contributed to a NMA for the MMSE outcome, and 51 RCTs with 19 329 patients contributed to a NMA for SAEs.

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

We will use a data-driven approach. More specifically, all IPD variables provided will be entered in our NMA and in the meta-regression analysis we will start by including one dependent and one independent variable. Then significant moderators will simultaneously be entered into multiple regression models as long as the minimum number of cases per independent variable is 10. Our goal is to avoid over-fitting and provide reliable treatment effect estimates.

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

The data we plan to abstract include study characteristics (eg, year of publication), aggregated patient characteristics (eg, number of patients), outcome results (eg, MMSE, SAE) and source of funding (categorised as: funded/authored by an employee of a drug manufacturer or other commercial organisation, government-sponsored/non-profit organisations, including universities and hospitals, no funding, funding unclearly reported, and funding not reported).[21] Two reviewers will abstract data independently, and all conflicts will be resolved through discussion. The year of publication and funding are potential effect modifiers. Therefore, these factors will be explored in a network meta-regression assuming a common fixed coefficient across treatment comparisons.

Statistical Analysis Plan: 

As with the original review, we will appraise the risk of bias using the Cochrane Risk of Bias tool.[22] We will draw a comparison-adjusted funnel plot[23] for both outcomes. Two review authors will also independently assess the quality of evidence in each NMA using the GRADE approach as extended for network meta-analysis.[24]

We will perform a Bayesian hierarchical random-effects meta-analysis for each treatment comparison, as we anticipate clinical and methodological between-study heterogeneity. We will perform a two-stage analysis, where at the first stage each individual will be analysed separately in each trial and at the second step the trial parameter estimates will be synthesised in a pairwise meta-analysis. All IPD from included studies will be first aggregated to study-level summary statistics using the R software (platform provided by the YODA project), and then these estimates will be introduced into the random-effects meta-analysis model. We will use the odds ratio for SAE[25] and the mean difference effect size for MMSE.[26] In case we are able to obtain IPD for a subset of trials, then we will use a two-part model with the same between-study variance in both parts and accounting for treatment-by-covariate interactions (including for example co-morbidities such as arrhythmias in the model[27]). The first part will entail the two-stage model described above using IPD only, whereas the second part will entail applying a pairwise meta-analysis with aggregate data.[27]
For a connected network of trials, the random-effects NMA model will be used. If possible, we will combine information across a network of trials using only IPD. If we are not successful in obtaining IPD for at least one study, we will combine both IPD and aggregated data in a single model. Again, a two-part analysis will be applied, considering the IPD reduced to aggregate data in the first part, and the aggregate data as identified in the published trials in the second part. Both IPD and aggregate data studies will share the same amount of heterogeneity. Information on patient-level covariates (eg, AD severity, sex) will be included in the model as secondary analyses. We will evaluate the consistency assumption using the design-by-treatment interaction model [28, 29] and the loop-specific method [30, 31] using aggregated data. If inconsistency is suggested, we will check the data for discrepancies and if none are identified, subgroup or meta-regression analyses will be performed.
We will estimate subgroup effects (eg, age, sex) using treatment-by-covariate interaction terms within studies and combining these across studies. We will apply 3 model specifications assuming that the regression coefficients are: a) different and unrelated across comparisons, b) different but related, sharing the same distribution, and c) identical across comparisons.[32, 33] We will compare the results of the models by evaluating the statistical significance of the regression coefficients for interactions, monitoring the reduction in the between-study variance, and using the Deviance Information Criterion[34] to compare the overall fit and parsimony of the models. We will rank the interventions for each outcome using the surface under the cumulative ranking curve.[35]

We will conduct multiple sensitivity analyses to examine the robustness of our results. We will: 1) restrict to studies with IPD only, 2) use different priors for the between-study variance, [36-38]3) restrict to RCTs with a low risk of bias, 4) use different imputation techniques for missing outcome data.[39, 40]

All pairwise meta-analyses and NMAs will be conducted using the Bayesian software OpenBUGS.[41] Two chains will be generated and convergence will be evaluated by their mixing, after discarding the first 10,000 iterations. We will use vague priors for all parameters of the models apart from the between-study variance for which we will use informative priors.[37, 38]

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/426">NCT00253227 - GAL-INT-2 - Galantamine in the Treatment of Alzheimer's Disease: Flexible Dose Range Trial</a></li><li><a href="/node/1043">NCT00679627 - GALALZ3005 - A Randomized, Double-Blind, Placebo-controlled Trial of Long-term (2-year) Treatment of Galantamine in Mild to Moderately-Severe Alzheimer's Disease</a></li><li><a href="/node/1556">NCT00216593 - GAL-ALZ-302 (PMID # 19042161-CR003940) - Treatment of Severe Alzheimer's Disease in a Residential Home, Nursing Home, or Geriatric Residential Setting: Evaluation of Efficacy and Safety of Galantamine Hydrobromide in a Randomised, Doubleblind, Placebo-Controlled Study</a></li><li><a href="/node/2371">GAL-93-01 - A group comparative, placebo-controlled, double-blind trial of the efficacy and safety of galantamine hydrobromide, 7.5 mg (6 mg galantamine base) TID, 10 mg (8 mg galantamine base) TID and 15 mg (12 mg galantamine base) TID taken orally for 12 weeks in patients with a diagnosis of senile dementia of the Alzheimer’s type</a></li><li><a href="/node/2696">GAL-USA-10 - Placebo-controlled evaluation of galantamine in the treatment of Alzheimer’s disease: Evaluation of safety and efficacy under a slow titration regimen</a></li></ol>

2017-1456

Project Title: 
The efficacy of biologic medications in improving depressive symptoms in patients with PsA – Patient-level meta-analysis
Specific Aims of the Project: 

We propose an individual-level meta-analysis using data from clinical trials among patients with PsA. We aim to examine how depressive mood changes with therapy intervention and the time course over which it changes. More specifically, we will compare the efficacy of the following two biologic medications for PsA: golimumab (TNF inhibitor) and ustekinumab (IL-12/IL-23 inhibitor) in improving depressive symptoms among patients with PsA participating in clinical trials.

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

Background: Psoriatic arthritis (PsA) is frequently associated with other co-morbidities including depression. Systemic inflammation is associated with depression, thus suppression of inflammation may have a beneficial effect on depressive symptoms.

Objective: To compare the efficacy of TNF inhibitor and IL-12/IL-23 inhibitor in improving depressive symptoms among patients with PsA participating in clinical trials.

Study Design: We will conduct a patient level meta-analysis of data from randomized placebo-control clinical trials assessing the efficacy of the following medications in patients with PsA: golimumab (TNF inhibitor) and ustekinumab (IL-12/IIndividual-patient data from each trial will be merged into a single dataset using common variables including demographics, co-morbidities including depression, measures of psl-23 inhibitor).

Participants: Patients with active PsA from clinical trials.

Main Outcome Measures: The primary outcome of the study is the change in depressive symptoms in study drug arm compared with the placebo arm. The Short-Form Health Survey (SF-36) mental component summary (MCS) score will be used assess the change in depressive symptoms over time.

Statistical Analysis:
Analyses will be conducted in the randomized set including all patients randomized to the study with complete data. The study outcome will be reported at all time points during the double-blind portion of the by exposure to the study drug or placebo.
The effect of each drug on depressive symptoms will be assessed using multivariable regression analysis.

Brief Project Background and Statement of Project Significance: 

Depression has wide reaching affects; it has been associated with an increased risk for cardiovascular disease, weight gain, increased musculoskeletal pain, and poor quality of life in the general population1, 2. These factors can have a significant impact on the course of other diseases including psoriasis and psoriatic arthritis (PsA). Psoriasis is common inflammatory skin disorder affecting 2-4% of the population3, 4. PsA is a chronic and often debilitating inflammatory arthritis affecting nearly one third of patients with psoriasis5, 6. Psoriatic disease (PsD), including both psoriasis and PsA, is associated with a number of comorbidities. Depression is among the most impactful on quality of life but has been given little attention in the literature7. However, recently, the awareness of this issue was heightened by concerns regarding drug-induced depression among patients with PsD participating in clinical trials. These events have underscored the importance of better understanding the relationship between depression and PsD and the impact of therapy for PsD on depression.
Depression is common among patients with PsA. The association between PsD and depression has been acknowledged for many years in the dermatology literature5, 6, 8. Patients with PsD may have debilitating joint disease with pain, fatigue, reduced ability to work, and embarrassment about joint deformities and skin disease. All of these factors can lead to depression. However, depression may also be related to the pathophysiology of PsD rather than soley a result of the clinical manifestations of PsD. In fact, depression may precede disease symptoms, particularly for PsA. Patients with psoriasis who were depressed were more likely to develop PsA than patients without depression9.
Inflammation and Depression. While depression has been primary thought of as a disregulation of neurotransmitters, recent evidence suggests that there are multiple processes at play, part of which is immune activation and inflammation. Previous meta-analyses have found that pro-inflammatory cytokines are associated with major depression. There is an overlap between some of the pro-inflamamtory mechnisms that have been linked with depression and PsD10,11. Thus, therapy for PsD may also have implications for depression. There is little data about the anti-depressant effects of biologic medications in humans10.
Significance: Despite this potential direct connection between the two disorders and the important clinical implications, little is known about the impact of effective control of inflammation on depressive mood in patients with psoriasis and PsA and the direct impact of therapies for PsD on depression. Furthermore, certain drugs may not be advantageous for patients with PsD and depression. Currently, there is no data about the relative efficacy of the various biologic medications in improving depressive symptoms in patients with psoriasis and PsA. The goal of this application is to examine how depression changes with treatment of PsD.

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

We will conduct a patient level meta-analysis of data from phase 2 and 3 randomized double-blinded placebo-control clinical trials assessing the efficacy of the following classes of medications in patients with psoriatic arthritis (PsA): 1) TNF inhibitors (golimumab); 2) IL-23/IL-12 inhibitor (ustekinumab);
The study population will include patients with active PsA that were treated with one of the 2 classes of biologic medications indicated above.

Inclusion criteria
1) Randomized placebo-control clinical trial in patients with active PsA
2) Phase 2 or 3 trials

Exclusion criteria
1) Open label study/extension period of RCTs
2) Phase 1 studies
3) Lack of SF-36 questionnaire response

For each trial the analysis will include all patients who were randomized to receive the study drug or placebo. Patients in the study drug arm will be compared to patients in the placebo arm. The comparison will include information collected during the double-blind portion of the trial. Information from the extension period of the trial will not be included in the analysis.

Narrative Summary: 

In this study we aim to compare the effect of three classes of medications used for the treatment of psoriasis and psoriatic arthritis in improving depressive symptoms in patients participating in clinical trials. The three classes of medications include: TNF inhibitors, IL-12/IL-23 inhibitors and IL-17 inhibitors.
We will combine data from clinical trials that assessed the effect of the above mentioned drugs. The study outcome will be the change in depressive symptoms that will be measured by a component of a quality of life questionnaire.
The results of the study will assist physicians treating patients with PsD in selecting the appropriate class of medication.

Project Timeline: 

The project will be completed over a period of 12 months. We have already obtained an exemption from ethics approval form the Women’s College Hospital REB (REB # 2017-0007-E Research Ethics Exemption Letter). The expected timeline:
March 2017 - Submit Requests for Data
December 2017 – completion of data analysis
March 2018 – Finish drafting the manuscript and submission for publication

Dissemination Plan: 

Knowledge dissemination strategies of our results will include a peer-reviewed and presentations at local and national and international medical conferences. We will also partner with patient organizations to disseminate the results of our study to the psoriasis and arthritis communities through publication in their websites (e.g., Arthritis Alliance of Canada, Arthritis Society, Canadian Association of Psoriasis Patients).

Bibliography: 

References
1. Iaquinta M, McCrone S. An Integrative Review of Correlates and Predictors of Depression in Patients with Rheumatoid Arthritis. Arch Psychiatr Nurs 2015;29(5):265-78.
2. Panagioti M, Scott C, Blakemore A, Coventry PA. Overview of the prevalence, impact, and management of depression and anxiety in chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2014;13(9):1289-306.
3. Michalek IM, Loring B, John SM, Takeshita J, Gelfand JM, Li P, Pinto L, Yu X, Rao P, Viswanathan HN, Doshi JA. A systematic review of worldwide epidemiology of psoriasis. LID - 10.1111/jdv.13854 [doi]
Psoriasis in the US Medicare Population: Prevalence, Treatment, and Factors Associated with Biologic Use. (1468-3083 (Electronic)).
4. Takeshita J, Gelfand JM, Li P, Pinto L, Yu X, Rao P, Viswanathan HN, Doshi JA. Psoriasis in the US Medicare Population: Prevalence, Treatment, and Factors Associated with Biologic Use. J Invest Dermatol 2015:[Epub ahead of print].
5. Dommasch ED, Li T, Okereke OI, Li Y, Qureshi AA, Cho E. Risk of depression in women with psoriasis: a cohort study. Br J Dermatol 2015:[Epub ahead of print].
6. McDonough E, Ayearst R, Eder L, Chandran V, Rosen CF, Thavaneswaran A, Gladman DD. Depression and anxiety in psoriatic disease: prevalence and associated factors. J Rheumatol 2014;41(5):887-896.
7. Ogdie A, Schwartzman S, Husni ME. Recognizing and managing comorbidities in psoriatic arthritis. Curr Opin Rheumatol 2015;27(2):118-26.
8. Kotsis K, Voulgari PV, Tsifetaki N, Machado MO, Carvalho AF, Creed F, Drosos AA, Hyphantis T. Anxiety and depressive symptoms and illness perceptions in psoriatic arthritis and associations with physicalhealth-related quality of life. Arthritis Care Res (Hoboken) 2012;64(10):1593-1601.
9. Lewinson R, Vallerand I, Lowerison M, Parsons L, Frolkis A, Kaplan G, Bulloch A, Patten S, Barnabe C. Depression and the Risk of Psoriatic Arthritis Among Patients with Psoriasis: A Population-Based Cohort Study [abstract 2164]. Arthritis Rheumatol 2016;68(Suppl 10).
10. Slyepchenko A, Maes M, Kohler CA, Anderson G, Quevedo J, Alves GS, Berk M, Fernandes BS, Carvalho AF. T helper 17 cells may drive neuroprogression in major depressive disorder: Proposal of an integrative model. Neuroscience and biobehavioral reviews 2016;64:83-100.
11. Barnas JL, Ritchlin CT. Etiology and Pathogenesis of Psoriatic Arthritis. Rheumatic diseases clinics of North America 2015;41(4):643-63.
12. Matcham F, Norton S, Steer S, Hotopf M. Usefulness of the SF-36 Health Survey in screening for depressive and anxiety disorders in rheumatoid arthritis. BMC musculoskeletal disorders 2016;17:224.

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
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: 

The primary outcome of the study is the change in depressive symptoms in study drug arm compared with the placebo arm. The Short-Form Health Survey (SF-36) mental component summary (MCS) score will be used assess the change in depressive symptoms over time.
The gold standard for assessment of depression is a psychiatric evaluation, however, various questionnaires were found to detect depression with reasonable sensitivity and specificity. The SF-36 is a generic measure of quality of life that is commonly measured in clinical trials to assess quality of life. A recent study found that the SF-36 MCS with a threshold of ≤38 could be used to detect major depressive disorder in rheumatoid arthritis patients with a sensitivity of 87%, sensitivity of 80% and accuracy of 83%.12
A secondary analysis will use the SF-36 MCS to classify patients based on depression status. We will use a cut-off of SF-36 MCS≤38 to identify patients with depression. We will perform sensitivity analyses with cut-off points of MCS≤35 and ≤40 as alternative definitions of depression. We will assess the change in depression status during t

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

The primary predictor of the study will be the use of a biologic medication. The reference group will be the use of placebo. For each trial the analysis will include all patients who were randomized to receive the study drug or placebo. Patients in the study drug arm will be compared to patients in the placebo arm. The comparison will include information collected during the double-blind portion of the trial. Information from the extension period of the trial will not be included in the analysis.

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

Individual patient data from each trial will be merged into a single dataset using common variables including demographics (age, sex), co-morbidities including depression, disease duration, measures of psoriasis and PsA disease activity including tender and swollen joint counts, dactylitis, enthesitis and psoriasis area and severity index, laboratory inflammatory markers including ESR and CRP, use of study drug and its dose or placebo, concomitant medications including disease modifying anti rheumatic drugs, non-steroidal anti-inflammatory medications, corticosteroids and anti-depressive medications and patient reported outcomes including SF-36 (physical and mental component scores), Health Assessment Questionnaire, patient global assessment of disease activity and pain scores.

Statistical Analysis Plan: 

We will perform the analysis in the combined dataset that will include all studies with complete individual-patient level data.
Analyses will be conducted in the randomized set including all patients randomized to the study with complete data. The study outcome will be reported at all time points during the double-blind portion of the study by exposure to the study drug or placebo.
Initially, each trial will be analyzed separately. Subsequently, the effect of each drug will be assessed individually and finally the effect of each drug class (TNFi or IL-12/IL-23) on depression will be assessed.
We will descriptively report the mean change in SF-36 MCS scores by study arm. Univariate linear regression models will be used to assess the effect of each study drug/drug class on the change in SF-36 MCS scores at the two follow up time points. Each model will also include a study indicator to account for population differences across studies. To assess what is the impact of baseline depression status on the effect of study drug/drug class on the change in Sf-36-MCS, a subgroup analysis by baseline depression status (patients meeting the criteria for depression based on SF-36-MCS cut-off) will be conducted. Subsequently, multivariable regression analyses will be performed to assess the effect of the study drug/drug class on the change in SF-36-MCS after adjusting for the following variables: age, sex, BMI, concomitant disease modifying anti-rheumatic drugs, previous biologic treatment, number of tender and swollen joints, minimal disease activity status, physician global assessment and PASI.
In addition we will consider depressive status as a categorical outcome by classifying patients to Depression or no-Depression based on a cut off of SF-36 MCS≤38.
We will descriptively report the number of patients meeting the criteria for depression by study arm and their demographic characteristics. We will next descriptively report the proportion of patients with depression at baseline who no longer meet criteria for depression at the two follow up time points. Similarly, we will report the number of patients without depression at baseline that become depressed in each group. We will use logistic regression models with depression status as the outcome and study drug/drug class as the primary predictor and baseline depression status as a model co-variate to assess the impact of the primary predictor on transition from baseline depression status to no meeting depression criteria at the two follow-up points. As described above, multivariable regression analyses will also be performed using the co-variates outlined above.
One of the advantages of the trials is that most include more than one dose of the study drug allowing for examination of dose effect. We will initially analyze each drug dose separately. We will assess whether a dose-response effect on depressive symptoms exists by comparing the effect sizes from the regression models across the different drug doses. We will then combine the different doses to a single group.
Sensitivity analyses: In studies in which depression indicators, either patient-reported depression, physician-reported depression or depression indices are included, we will report the results using both definitions of depression.
In studies with <10% missing data, can use multiple imputation to see if results are different (or use this as primary. A complete case analysis will be conducted. We will examine whether there are differences at baseline among those with and without missing data.
The primary analysis will be performed according to the intention to treat principle with the outcome measure assessed at the first escape point (approximately 12 to 16 weeks). A secondary analysis will assess the outcome at the second follow up period (24 weeks). A sensitivity analysis will be performed per-protocol at each follow up time point.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/557">NCT00265096 - C0524T08 - A Multicenter, Randomized, Double-blind, Placebo controlled Trial of Golimumab, a Fully Human Anti-TNFa Monoclonal Antibody, Administered Subcutaneously in Subjects with Active Psoriatic Arthritis</a></li><li><a href="/node/558">NCT01009086 - CNTO1275PSA3001 /// PSUMMIT I - A Study of the Safety and Effectiveness of Ustekinumab in Patients With Psoriatic Arthritis</a></li><li><a href="/node/559">NCT01077362 - CNTO1275PSA3002 /// PSUMMIT II - A Study of the Safety and Efficacy of Ustekinumab in Patients With Psoriatic Arthritis With and Without Prior Exposure to Anti-TNF Agents</a></li></ol>

2017-1356

Project Title: 
Does Body Mass Index predict efficacy of abiraterone acetate therapy in patients with metastatic castration-resistant prostate cancer?
Specific Aims of the Project: 

The objective of this study is to use data from a large clinical trial of drug therapy in men with metastatic prostate cancer to identify whether BMI could act as a predictor of the efficacy of abiraterone acetate reffering to progression-free survival and overall survival.
Hypothesis: We supposed that excess body weight, as measured by BMI, may lead to the treatment resistance to the abiraterone of prostate cancer.

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

Background: The increase in prostate cancer incidence and mortality observed in immigrants from low-risk to high-risk countries suggests that lifestyle and dietary factors play an important role in the etiology of prostate cancer. Excess body weight comprehensively reflects lifestyle and dietary factors, which occurs when the expenditure (i.e., physical activity) is less than the intake (i.e., high-fat diets).[1-3] Excess body weight, as measured by BMI, has been considered a factor for decreased and increased risk of localized and advanced prostate cancer, respectively. However, the relationship between BMI and efficacy of abiraterone acetate therapy remains unclear.
Objective: The objective of this study is to use data from a large clinical trial of drug therapy in men with metastatic prostate cancer to identify whether BMI could provide some indication of efficacy of abiraterone acetate.
Study Design: Retrospective cohort study.
Participants: mCRPC patients from COU-AA-302 and COU-AA-301 treated with abiraterone or placebo.
Main Outcome Measures(s): Outcomes evaluated will include PSA progression-free survival, overall survival, progression free survival as well as response to subsequent therapies.
Statistical Analysis: Cox regression analysis will evaluate the role of BMI as a prognostic biomarker. Analyses will be stratified by treatment received, ECOG status, LDH, hemoglobin level, Gleason score, TNM stage and age, et al.

Brief Project Background and Statement of Project Significance: 

Excess body weight, as measured by BMI, has been considered a factor for decreased and increased risk of advanced prostate cancer. There is a complex array of biological mechanisms through which obesity may influence prostate carcinogenesis and metastasis, including hyperinsulinemia, elevated insulin-like growth factor (IGF) hormone levels, dysregulation of sex steroid hormones, altered levels of adipokines, and chronic inflammation.[4-6] Obesity is also associated with chronic inflammation and biomarkers of inflammation in the body, such as higher levels of C-reactive protein, which have been associated with prostate cancer–specific mortality. [7,8] Obese men have been shown to exhibit reduced levels of androgens, and there is evidence that men with lower levels of testosterone have more aggressive tumors at clinical presentation.
Abiraterone functions by interference with steroid metabolism . Normally in the adrenal glands, adrenocorticotropic hormone (ACTH) stimulates metabolism of the steroid precursor pregnenolone. Pregnenolone can be further metabolized to aldosterone or to 17OH-pregnenolone, a common precursor for cortisol and testosterone. The action of 17[alpha]-hydroxylase converts pregnenolone to 17OH-pregnenolone, and 17,20-lyase further converts this product to dehydroepiandrostenedione (DHEA). DHEA is subsequently converted to an intermediary and finally testosterone. Abiraterone is a potent inhibitor of the 17[alpha]-hydroxylase and 17,20-lyase enzymatic functions of CYP17.[9] Recent preclinical work has also identified [DELTA]4-abiraterone, an active metabolite of abiraterone, that further inhibits 3[beta]-hydroxy steroid dehydrogenase, CYP17A1, and 5[alpha]-reductase. In the presence of ACTH stimulation and abiraterone, pregnenolone is shunted to mineralocorticoid synthesis. Abiraterone used without replacement corticosteroids to suppress ACTH results in a syndrome of mineralocorticoid excess.[10] Abiraterone thus was studied in conjunction with corticosteroids in its clinical development.
As a result, we supposed that excess body weight, as measured by BMI, may lead to the treatment resistance to the abiraterone of prostate cancer.

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

Data source: COU-AA-302 and COU-AA-301
Inclusion criteria: all patients in the trial
Exclusion criteria: missing data

Narrative Summary: 

Recent studies have found that risk varies by stage of disease, tumor grade, and cause-specific mortality. Several meta-analyses have indicated that greater body mass index (BMI) is associated with increased risks of aggressive/advanced prostate cancer and prostate cancer– specific mortality, but the relationships for BMI and efficacy of abiraterone acetate therapy remain inconclusive. The aim of this study is to use data from a large clinical trial of drug therapy in men with metastatic prostate cancer to identify the relationship between BMI and efficacy of abiraterone acetate. The results may help establish an economical and accessible biomarker to predict the efficacy of abiraterone.

Project Timeline: 

Project start date: 3/2017
Analysis completion date: 4/2017
Date manuscript drafted/submitted: 5/2017
Results reported 8/2017

Dissemination Plan: 

We plan to publish the results of this project in the form of a manuscript in oncology and urology medical journals.

Bibliography: 

1. Cao Y, Ma J. Body Mass Index, Prostate cancer–specific mortality, and biochemical recurrence: A systematic review and meta-analysis. Cancer Prev Res.
2011;4(4):486–501.
2. Zhong S, Yan X, Wu Y, et al. Body mass index and mortality in prostate cancer patients: A dose-response meta-analysis. Prostate Cancer Prostatic Dis.
2016;19(2):122–31.
3. Chen Q, Chen T, Shi W, et al. Adult weight gain and risk of prostate cancer: A dose-response meta-analysis of observational studies. Int J Cancer. 2016;
138(4):866–874.
4. Renehan AG, Zwahlen M, Egger M. Adiposity and cancer risk: New mechanistic insights from epidemiology. Nature Reviews Cancer. 2015;15(8):484–498.
5. Hsing AW, Gao YT, Chua S, et al. Insulin resistance and prostate cancer risk.
J Natl Cancer Inst. 2003;95(1):67–71.
6. Albanes D, Weinstein SJ, Wright ME, et al. Serum insulin, glucose, indices of
insulin resistance, and risk of prostate cancer. J Natl Cancer Inst. 2009;101(18):
1272–1279
7. De Marzo AM, Platz EA, Sutcliffe S, et al. Inflammation in prostate carcinogenesis. Nat Rev Cancer. 2007;7(4):256–269.
8. Platz EA, De Marzo AM. Epidemiology of inflammation and prostate cancer.
J Urol. 2004;171(2 Pt 2):S36–S40
9. Sternberg CN, Petrylak DP, Madan RA, et al. Progress in the treatment of advanced prostate cancer. Am Soc Clin Oncol Educ Book 2014:117-31.
10. Attard G, Reid AH, A’Hern R, et al. Selective inhibition of CYP17 with abiraterone acetate is highly active in the treatment of castration-resistant prostate cancer. J Clin Oncol 2009;27:3742-8.

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

Date of death (overall survival)
Date of PSA progression (PSA progression-free survival)
Date of Radiographic PFS (Radiographic progression-free survival)

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

Body Mass Index (continuous)

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

In this study, we will not only focus on one predictor. We seek to investigate the variables associated with all cause mortality and disease progression. The variables of interest include:
Age at study entry (continuous)
Race
Height (continuous)
Treatment Arm (Abiraterone or placebo)
Gleason Score (Categorized)
Date of Diagnosis
Presence of liver metastases (Present/Absent)
Presence of bone metastases (Present/absent)
Presence of nodal metastases (Present/absent)
Presence of visceral metastases (Present/absent)
Time from start of initial LHRH to abiraterone treatment (continuous)
Weight (kg, each visit record from inclusion to the end of follow-up)
Prior anti-cancer therapies (number of prior hormonal therapies, prior ketoconazole, prior chemotherapies(COU-AA-302))
Prior prostatectomy and/or radiation therapy (Y/N for each)
Investigations (PSA, Hgb, Cr, AlkPhos, LDH)
Pain score / presence of pain (binary Y/N)
Performance Status (ECOG)
Mode of progression (clinical, radiographic, toxicity)
Best PSA response (% reduction)
Date of Abiraterone or Prednisone initiation
Adverse events or complications occurred during the treatment

Statistical Analysis Plan: 

Descriptive statistics will assess median BMI values at baseline, and at each time of visit during subsequent follow up for placebo or AA treatments. Baseline BMI (< or > 25 or alternate cut-off) values will be compared for differences in known baseline prognostic factors such as LDH, Hgb, AlkPhos, ECOG, pain status, presence of metastases and PSA, et al. Univariate and multivariate cox regression analyses will evaluate the HR of baseline and increases or decreases (based on linear regression of changes over time) in BMI values on outcomes of OS, PFS and response to subsequent therapies. This will be performed separately for both arms of the trial based on treatment received. Area-under-the curve analyses will compare the relative predictive ability of BMI to predict response to AA as measured by best PSA response. All statistical tests will be done using R statistics package, version 2.8.1(http://www.r-project.org/).

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>

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