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2016-1005

Project Title: 
Identification of multivariate, clinical patterns predicting treatment response to paliperidone in schizophrenia
Specific Aims of the Project: 

To generate multivariate statistical models based on clinical data to predict treatment response to paliperidone in patients with schizophrenia on the level of individual subjects.

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

Application Status

Ongoing
Scientific Abstract: 

Background:
Only a subset of patients with schizophrenia repond sufficiently to the currently available pharmacological interventions. There exists no clinical marker to predict treatment response in individual patients. This inefficacy leads to prolonged illness episodes and suffering in affected patients, repeated hospitalizations and a substantial socio-economic burden.

Objective:
To use clinical data from existing clinical trials to generate multivariate models for the prediction of treatment response to paliperidone in patients with schizophrenia.

Study Design:
Clinical and demographic variables of individual patients will be used to generate prediction models. We will employ a cross-validation scheme (leave-one-trial-out cross-validation) to estimate the generalizability of the generated models.

Participants:
All participants of the requested trials will be included in the analysis.

Main Outcome Measure(s):
The main outcome measure of our analysis will be the balanced accuracy ([sensitivity + specificity]/2) of the generated multivariate statistical models when predicting early responders vs. early non-responders (week 6-7) and late responders vs. late non-responders (week 12-13). Response to paliperidone will be defined as a reduction on the PANSS total of >20% relative to baseline.

Statistical Analysis:
All continuous variables will be mean centered and scaled. A wrapper method will be used to generate models based on a minimal set of highly predictive variables.

Brief Project Background and Statement of Project Significance: 

The clinical response to pharmacological interventions in patients with schizophrenia is highly heterogenous. Despite significant scientific efforts, there exists currently no reliable biomarkers to predict treatment response. As a consequence, patients frequently undergo multiple unsuccessful pharmacological interventions until they respond sufficiently. This leads to prolonged illness episodes and suffering in affected patients, repeated hospitalizations and a significant socio-economic burden. In the absence of more effective treatment options, it has been suggested that a stratification prior to treatment initiation could be a valuable strategy to improve treatment outcome in psychiatric patients (Chekroud & Krystal 2016, BMJ). In their recent work Chekroud et al. (2016, Lancet Psychiatry) demonstrated that merely based on clinical and demographic data, a multivariate statistical model could be used to predict treatment response in patients with major depression. In this study, we are planning to apply this approach to existing data from pharmacological trials in patients with schizophrenia. Multivariate pattern analysis (MVPA) will be employed to generate statistical models to predict treatment response to paliperidone. The analysis will identify a minimal subset of highly predictive variables. Those could be used to create a MVPA-based clinical questionnaire to be used in the clinical setting to assess patients and acquire the necessary data for response prediction. In this way, our work could significantly contribute to the efficacy of existing pharmacological treatments in psychiatry. If applied in the clinical setting, patients could be assessed prior to treatment initiation and their treatment response could be predicted using the generated models. Treatment with paliperidone could then be restricted to only those patients with sufficient response while other patients could be treated with other compounds.

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

Multivariate pattern analysis (MVPA) is a new statistical method that allows the identification of predictive patterns in high-dimensional data sets. In comparison with traditional univariate statistical approaches, MVPA is particularly suited to be applied to a large number of variables. The approach allows prediction with high accuracy on the level of individual subjects. In this way, models can be applied to answer practical questions in the clinical context such as the prediction of the disease course or potential differential diagnosis of an individual patient. Thus, the aim of the present project is to apply MVPA to identify patterns in clinical data to predict treatment response to paliperidone in patients withs schizophrenia.

In addition, we will apply trajectory-based statistical methods to these data. Trajectory-based models (e.g. latent class models, and growth mixture models) capture heterogeneity in the development of clinical outcomes during an intervention, and this more sensitive approach can result in trial outcomes that differ from traditional endpoint measures.

Narrative Summary: 

The response to existing pharmacological interventions in patients with schizophrenia is highly heterogenous. Overall, it is estimated that between 20 and 30 % of patients do not respond sufficiently to medication. Currently, clinicians have no way of predicting whether a specific medication will work for a specific patient. Consequently, patients frequently undergo multiple unsuccessful treatments. This is associated with a prolonged duration of illness episodes, repeated hospitalizations and increased financial and medical costs. We plan to develop tools that can help clinicians choose better treatments, and ultimately help patients get better faster.

Project Timeline: 

We plan to initiate the project at the 1st of October 2016. At the initial stage of the project, data will be merged and processed to prepare analysis. At the 1st of November we will begin the analysis of the prepared data and expect results at the 1st of January 2017. In the following period the draft will be a written and prepared for publication. We expect the first submission of the manuscript at the 1st of March 2017.

Dissemination Plan: 

We plan to submit the finalized manuscript to one of the leading, peer-reviewed journals in the field of schizophrenia and psychiatry (e.g. Molecular Psychiatry, Biological Psychiatry, American Journal of Psychiatry, Schizophrenia Bulletin, JAMA Psychiatry, Lancet Psychiatry). Furthermore, we plan to create a clinical questionnaire to specifically provide the required information to predict treatment response based on our multivariate models, and make this available to clinicians online. This will allow this research to reach its maximum potential for improving patient care.

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

The main outcome measure of our analysis will be the balanced accuracy ([sensitivity + specificity]/2) of the generated multivariate statistical models when predicting early responders vs. non-responders (week 6-7) and late responders vs. non-responders (week 12-13) (http://www.ncbi.nlm.nih.gov/pubmed/14662555). Response to paliperidone will be defined as a reduction on the PANSS total of >20% relative to baseline. For trajectory based approaches, we will use trajectories of PANSS-based outcome measurements over time.

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

In the current project, we plan to employ a data-driven approach. All variables shared between the available clinical trials will be entered in our analysis. Using cross-validation and penalized statistical methods we will aim to avoid overfitting and generate reliable estimated of the generalizability of the models predictions.

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

We will test the specificity of our results to paliperidone by applying the generated models to predict early and late treatment response (reduction in PANSS total score >20% relative to baseline) in patients treated with quetiapine, olanzapine, aripiprazole and placebo.

Statistical Analysis Plan: 

We will employ a multivariate approach to generate statistical models to predict treatment response to paliperidone. The main outcome measure will be the binary variables early treatment response (week 6-7) and late treatment response (week 12-13) whereas treatment response will be defined as a reduction in the PANSS total score of >20% with respect to baseline. From all trials only participants with available data of at least one of the main outcome measures will be included. Based on those participants we will define maximum set of clinical and demographic variables that is shared between all trails. Prior to analysis all continous variables will be mean centered and scaled. In order to improve predictive power of the modes, variables with low predictive value as well as highly correlated variables will be removed from the analysis to retain a subset of n=25 highly predictive variables (Chekroud et al., 2016). This will be accomplished by a elastic net regression which includes a linear combination of a L1- as well as a L2-regularization term. Subsequently, we will fit a gradient-boosting machine to classify patients into responders and non-responders. Most importantly, all data processing steps will be embedded in a nested cross-validation scheme. At the outer level of the cross-validation we will successively hold out one entire trial and use the remaining data to generate the predictions models. Then the models will be applied to the hold-out data to estimate the generalizability of the model. This procedure will be repeated until each trial has been held out once. At the inner cross-validation loop, we will employ a 10-fold cross-validation with 10 permutations to optimize the multivariate prediction models. The main outcome measure of the prediction models will be the balance accuracy.
In order to test the specificity of our results to paliperidone, the models will be applied also to participants receiving other drugs (risperidone, quetiapine, olanzapine, aripiprazole) or placebo. In this way, we will test whether the identified predictive patterns relate to a specific paliperidone response, a general trait to respond to any medication or a general subtype of patients with a spontaneous remission.
For trajectory-based approaches we will explore whether similar or different trajectory classes exist for patients who take active treatments or placebo, and teste whether there were clinical predictors of trajectory class membership.

Sample Size Addendum:
Our thoughts on sample size are driven primarily by data availability. As you know, machine learning approaches depend critically on learning from large samples. Traditional power analyses do not apply in the same way as they would say, for a clinical trial, because algorithms will almost always benefit from additional subjects. In addition, until we have access to the data and can ascertain the level of missingness in baseline data, we would not be able to know exactly how many patients we would be able to use across all of the studies. We will us multivariate approaches with dimensionality reduction built in (e.g. decision trees, or "penalized" approaches such as lasso or ridge regression), which greatly minimizes concerns about our ability to include a large number of predictor variables in our models.

Finally, to address the issue of whether the lack of efficacy is due to non-adherence to study medications, we can conduct sensitivity analyses by restricting our analyses to a sample of patients that self-reported adhering to medications. Despite these provisions, it is difficult to differentiate amongst reasons for efficacy (for example, whether the individual patient would have got better anyway, whether the patient had a placebo response, or it was due to the active compound). Our primary goal is to develop easy to use tools that predict treatment outcome for a given intervention, rather than to try and specifically predict the reason for that outcome

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/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/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/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/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/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/1032">NCT00645307 - R076477-SCH-701 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group Study With an Open-Label Extension Evaluating Extended Release OROS® Paliperidone in the Prevention of Recurrence in Subjects With Schizophrenia - Open Label Phase</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2017

2016-0969

Project Title: 
Identifying individual factors predictive of extrapyramidal side effects (EPS) in Alzheimer's disease
Specific Aims of the Project: 

Aims
(i) Develop a PK model for risperidone, using PK data from the above trials. The model would include CYP status and specific covariates of interest (age, gender, renal function, height, weight, smoking, race, concomitant medications (specifically CYP substrates))
(ii)Use model outputs to simulate average steady state concentration (Cav) of (risperidone and active metabolite) across the prescribed dose range in the above trials.
(iii) Develop Cav-response and Cav-EPS models, and include drop-out and placebo response
(iv) Use model predictions (taking into account covariates which contribute to inter-individual variability) to establish the whether specific doses of risperidone(0.5mg. 1mg. 1.5mg, 2mg) achieve or exceed the target concentration range.

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

Application Status

Incomplete
Scientific Abstract: 

Background: Individual factors that predict antipsychotic sensitivity are poorly understood in Alzheimer’s disease (AD). Research which aims to further inform age and AD- specific dose adjustments and identify patient characteristics which predict response and side effects will be a key step towards improving safety profiles
Study Design: Population approach (non-linear mixed effects modelling) to investigate the relationship between average steady state risperidone concentration, clinical response (reduction in delusions and hallucinations) and extrapyramidal side effects (EPS) in psychotic AD patients.
Participants: Patients with probable or possible AD, included in the listed trials above, who have psychotic symptoms at baseline (a score of 2 or more on any item of the BEHAVE-AD psychosis subscale at screening).
Main Outcome Measure: Clinical response (25% reduction in psychotic symptoms); EPS (emergent EPS, defined as scores >3 on Simpson Angus).
Statistical Analysis: Population pharmacokinetic-pharmacodynamic models will be developed to establish the relationship between steady state risperidone concentration, and the probability of clinical response (25% reduction in psychotic symptoms) or EPS (defined by simpson angus or other motor rating scale). The analysis will take into account placebo and drop out

Brief Project Background and Statement of Project Significance: 

The mechanisms underpinning antipsychotic sensitivity are poorly understood in Alzheimer’s disease (AD) and research which aims to understand the relationship between inter-individual variability in pharmacokinetics (PK) and clinical outcome will help to guide dose adjustments. The population approach is a potentially useful tool in this respect as it used non-linear mixed effects modelling to establish the consistency and identify sources of variability in dose-PK and PK-response data, with the aim of making predictions about a typical person the population of interest.
Previous studies which have used a population approach to explore PK profiles of psychotropic drugs in older AD patients have produced mixed findings. For example, PK models developed using data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trials for Alzheimer’s disease (AD) and schizophrenia (SZ) have shown a significant effect of age on clearance of the active metabolite (9-OH risperidone) of risperidone (Feng et al, 2008), whereas inter-individual variability in olanzapine clearance was accounted for by factors other than age (gender, smoking and African-American race) (Bigos et al, 2008b). These data, and a recent publication from the Citalopram in Alzheimer’s disease (CitAD) study, which showed significant and clinically relevant effects of age and gender on metabolic clearance of R- but not S-citalopram (Akil et al. 2016), serve to emphasise the importance of extending pharmacological modelling to representative older clinical populations, to meaningfully refine and optimise age- and disease- specific dose adjustments.
The proposed study would use a population approach to characterise dose-PK and PK-outcome relationships during risperidone prescribing, with the aim of using model simulations to establish the dose adjustments required to achieve a target therapeutic range, in which symptom reduction would not be accompanied by EPS.

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

Above trials
Inclusion as described in the trials, with the addition of the presence of psychotic symptoms (indexed by a score of 2 or more on any item of the BEHAVE-AD psychosis subscale at screening)
Exclusion criteria as described in the above trials

Narrative Summary: 

Older people with dementia represent a group at highest risk of side effects and stroke during antipsychotic use. Understanding the individual characteristics that predict response and side effects when antipsychotics are used off license to treat psychotic symptoms in dementia will be a key step towards improving safety profiles. The proposed study aims to establish if there is a clear relationship between average steady state risperidone concentration and response (reduction in psychotic symptoms)/side effect profile in patients with Alzheimer's disease, taking into account placebo and dropout.

Project Timeline: 

Start data: Aug 2016
Timelines:
Extract data /set up appropriate database /spreadsheet to model data in either Monolix or NON-MEM (1 month)
Develop PK-PD models/simulations (3 months)
Manuscript preparation/revisions (2 months)
This will be completed within 6 months

Dissemination Plan: 

JAMA Psychiatry
Am J Psychiatry

Bibliography: 

Feng Y, Pollock BG, Coley K, Marder S, Miller D, Kirshner M, et al. Population pharmacokinetic analysis for risperidone using highly sparse sampling measurements from the CATIE study. British journal of clinical pharmacology. 2008;66(5):629-39.

Bigos KL, Pollock BG, Coley KC, Miller del D, Marder SR, Aravagiri M, et al. Sex, race, and smoking impact olanzapine exposure. Journal of clinical pharmacology. 2008;48(2):157-65.

Akil A et al. (2016) A population pharmacokinetic model for R- and S-citalopram and desmethylcitalopram in Alzheimer's disease patients with agitation J Pharmacokinet Pharmacodyn 43:99-109 doi:10.1007/s10928-015-9457-6

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

Reduction in psychotic symptoms, defined by Psychosis Cluster Score of Pathology from the Behavioral Pathology in Alzheimer's Disease (BEHAVE-AD)
EPS - emergent EPS will be defined as scores of 3 or more on Simpson Angus Scale

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

Average steady state concentration risperidone and active metabolite

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

Covariates
AGE (continuous)
GENDER
CrCL - ml/min (continuous)
Comcomitant medication (will need to be examined and categorised)
smoking status (binary)
CYP status

Statistical Analysis Plan: 

Analysis will be conducted under the supervision of the London Pharmacometric Group (UCL) and will include both descriptive and population-based analysis
(i) Compartmental PK models will be used to describe the time course of plasma drug concentrations on PK parameters. A nonlinear mixed-effects model will be developed to simultaneouslydescribe risperidone and 9-OH risperidone concentration-time profile. Covariate effects on risperidone and 9-OH risperidone PK parameters will be assessed, including age, weight, sex, smoking status, race and concomitant medications. If possible (if cyp2d6 genetics are available), CL will evaluated using a mixture model, which separates [poor metabolizer (PM), extensive metabolizer (EM) and intermediate metabolizer (IM)].
(ii) The combination of an Emax and the Weibull model will be used to describe drug and placebo effects. An exponential model will be used to identify the predictors of probability of dropout. Simulations will be performed to establish target concentration to elicit a response (using 25% reduction in target symptom).
(iii) EPS will be modelled using a continuous time probability model with Markov elements, taking into account placebo and drop-out.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/850">NCT00249158 - RIS-AUS-5/CR006010 - Risperidone in the Treatment of Behavioural and Psychological Signs and Symptoms in Dementia (BPSSD): a Multicentre, Double-blind, Placebo-controlled Parallel-group Trial</a></li><li><a href="/node/854">NCT00249145 - RIS-INT-24/CR006046 - Risperidone in the Treatment of Behavioral Disturbances in Demented Patients: an International, Multicenter, Placebo-controlled, Double-blind, Parallel-group Trial Using Haloperidol as Internal Reference</a></li><li><a href="/node/862">NCT00034762 - RIS-USA-232/CR002764 - Efficacy And Safety Of A Flexible Dose Of Risperidone Versus Placebo In The Treatment Of Psychosis Of Alzheimer's Disease</a></li><li><a href="/node/865">NCT00253123 - RIS-USA-63/CR006022 - A Randomized, Double-Blind, Placebo-Controlled Study of Risperidone for Treatment of Behavioral Disturbances in Subjects With Dementia</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2017

2016-0960

Project Title: 
Bayesian Methods for Comparative Effectiveness Research
Specific Aims of the Project: 

Specific Aims:
1) Extend a Bayesian Dirichlet process mixture model for time-to-event and count data.
2) Demonstrate the behavior of the hierarchical Bayesian Dirichlet process mixture model in the presence of varying degrees of between-study heterogeneity.
3) Create a publicly available R package for carrying out meta-analyses using a Bayesian Dirichlet process mixture model.
4) Evaluate the Bayesian Dirichlet process mixture model with propensity score adjustment.

Objectives:
• Evaluate the usefulness of a Bayesian Dirichlet process mixture model for meta-analysis when studies are not exchangeable.
• Include propensity score adjustment in the Bayesian Dirichlet process mixture model and evaluate the resulting inference, compared to a non-mixture model analysis.

Hypotheses:
• The Bayesian Dirichlet process mixture model will provide better inference than commonly used hierarchical models when studies are not fully exchangeable.
• The Bayesian Dirichlet process mixture model will be able to adjust for imbalances better than other regression-type approaches, since it does not assume a linear relationship on some scale.

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

Application Status

Ongoing
Scientific Abstract: 

Background: Comparative effectiveness research (CER) focuses on evaluations of treatment strategies in real-world settings. A treatment’s effectiveness information may come from randomized clinical trials (RCTs), non-randomized studies, and hospital or payer databases. The Bayesian inferential paradigm provides a structured way to combine information from disparate sources within the framework of probability theory. Bayesian nonparametric (BNP) models place fewer restrictions on assumed mathematical forms of underlying probability distributions, and this flexibility may improve inference concerning key parameters, such as treatment effects.

Objective: We propose to extend a published BNP model, the hierarchical Dirichlet process mixture model, in a CER analysis inferring a treatment’s effectiveness from multiple randomized clinical trials and possibly observational data. We will also evaluate this model with propensity score adjustment.

Study Design: We will apply our BNP model to analyze patient-level data from 8 RCTs evaluating Epoetin alfa. We may also perturb the raw data from some RCTs to mimic non-RCT data to evaluate the model.

Participants: All patients in the 8 studies for whom we have the same information, e.g., hemoglobin change, transfusion rate.

Main Outcome Measures: The likely outcome measures for our analyses will be hemoglobin and transfusion rate (if all 8 RCTs include these outcomes).

Statistical Analysis: We will apply the BNP hierarchical model to analyze the data and compare inferences to results from less flexible meta-analytic models.

Brief Project Background and Statement of Project Significance: 

Comparative effectiveness research often combines relevant data from disparate sources. Data about a treatment’s effectiveness may come from randomized clinical trials (RCTs), non-randomized studies, and hospital or payer databases. Appropriate statistical models for combining RCT data with information from patients undergoing the same treatment strategy in clinical practices should help comparative effectiveness inferences.
The Bayesian inferential paradigm provides a structured way to combine information from disparate sources within the framework of probability theory. The ultimate question is how best to treat the next patient who enters the clinic. In Bayesian statistics, this question relates to the predictive distribution. Given the current data, how likely are key outcomes if one chooses to treat this patient with the new treatment or with a different one? The Bayesian inferential framework allows one to answer this and related questions.
Heterogeneity between sources of data may exist, and one needs to account for inherent differences for valid inference. For example, RCT patients satisfy strict entry criteria. Also, outcomes in the RCT setting with its prescribed supportive care may well differ from outcomes among eligible patients at the same institution who decline to participate or from outcomes in community hospital settings whose patients may not have access to the same level of care. One may model these differences as covariates when these covariates are available, but unmeasured factors may also affect the treatment outcome.
Regression approaches infer the effects of covariates as shifts of expected values on some scale (e.g., linear or logistic). Differences in the patient populations may alter the underlying distributions beyond a simple shift in the location and lead to between-study differences for which regression methods are inadequate. Bayesian nonparametric (BNP) methods provide a more flexible approach to inference. One approach is based on mixtures of simple distributions. For example, mixtures of normal distributions can characterize almost any distribution, be it skewed, multimodal, etc.
We have proposed flexible Bayesian analytic methods based on mixtures for meta-analysis. The model decomposes the distribution of parameters into a common distribution shared by all studies and study-specific distributions. The final distribution is a weighted average of the common and study-specific distributions. The common and study-specific distributions may be finite mixture models (Lopes, Müller, and Rosner, 2003) or infinite Dirichlet process (DP) mixtures (Müller, Quintana and Rosner, 2004). The models also allow nonparametric regression on covariates.
Earlier papers did not fully evaluate the hierarchical DP mixture model with a large number of studies. We propose applying the model to analyze RCT data. Aside from analyses of study data, we will also evaluate how robust the inference is by perturbing data from some of the RCTs and analyzing the resulting data. We feel that BNP models will allow one to evaluate effectiveness by including more relevant data sources than other models allow.

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

We will evaluate out methodology by analyzing data from all patients who participated in the randomized trials evaluating erythropoietin and whose data are available at YODA. Since our objective is to evaluate a statistical method, we will not apply any inclusion or exclusion criteria for selecting patients.

Narrative Summary: 

Comparative effectiveness research entails combining relevant data from disparate sources. Information about the effect and effectiveness of a treatment for a disease, such as cancer or heart disease, may come from randomized clinical trials, early phase non-randomized studies, and hospital or payer databases. One needs to account for inherent differences between the studies, however, because heterogeneities may bias the inference. This project will develop a flexible inferential model using Bayesian nonparametric methods to characterize prior uncertainty and allow for borrowing strength when appropriate, while also accommodating heterogeneities between data sources.

Project Timeline: 

We plan to have the R program ready in 3 months. The initial analyses will be finished 7 months after we gain access to the data. We plan to have an initial manuscript ready by the end of 12 months.

Dissemination Plan: 

We will publish the method and the analyses (as a case study) in a statistical journal, such as “Biometrics” or “Biostatistics.”

We will publish a separate manuscript describing incorporation of propensity score adjustment. We will publish this paper in a statistical journal, such as “Biometrics,” or in a journal with more of a clinical trials focus, such as “Clinical Trials.”

We will provide an R package for implementing our model as part of the publication.

Bibliography: 

Lopes HF, Müller P, Rosner GL. Bayesian meta-analysis for longitudinal data models using multivariate mixture priors. Biometrics. Mar 2003;59(1):66-75.
Müller P, Quintana F, Rosner GL. A Method for Combining Inferences Across Related NonParametric Bayesian Models. Journal of the Royal Statistical Society Series B-Statistical Methodology. 2004;66(3):735-749.

What is the purpose of the analysis being proposed? Please select all that apply.: 
Other
Please Explain: 
Development of new statistical methods
Submit Data Request: 
Main Outcome Measure and how it will be categorized/defined for your study: 

The main outcome measures for this project will be the outcomes of the randomized clinical trials we are requesting. The requested trials of erythropoietin do not appear to have the same primary endpoints, necessitating us to find a meaningful clinical endpoint that each data set contains. The two most likely outcomes will be hemoglobin and transfusion rate (number of transfusions per unit time, such as one or more months).

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

We will evaluate known covariates that affect the outcome, such as baseline hemoglobin. Additionally, we will use all covariates that are common to the randomized trials for the purpose of developing propensity scores.

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

There are no other variables of interest.

Statistical Analysis Plan: 

We will analyze the data using the hierarchical Dirichlet process mixture (HDPM) model described in the Background section. We will compare the results to inferences using a Bayesian hierarchical model that makes more assumptions about the distribution of model parameters across studies. We will also evaluate the robustness of the HDPM model by perturbing the data in some studies to mimic registries and carrying out an analysis of all studies. We will also evaluate the use of propensity score adjustment in the context of the HDPM model.

We may have data from fewer than 8 studies to use for our analysis. Our goal is to evaluate a Bayesian nonparametric (NP) model for use in comparative effectiveness research (CER), not to carry out a meta-analysis. We plan to use data from these trials primarily to create an environment similar to what one might encounter in CER. We have not found randomized clinical trial (RCT) data and a publicly available registry relating to the same treatment. We want to use these trials’ data as comprising a hypothetical registry plus RCTs’ data. E.g., we may accomplish the CER scenario by treating data from the active EPO alpha arm of one or two RCTs as registry data, ignoring each trial's comparator arms. By combining these “registry” data with data from the remaining 6 RCTs, we can imitate a situation that could arise in practice. The actual clinical trial data allow for a more realistic evaluation than would simulation data. Of course, the number of RCTs with usable (for our purpose) outcome data may turn out to be fewer than eight. As long as we have at least 4 RCTs, we feel we can carry out our intended analyses. We also propose evaluating the use of propensity scores within a Bayesian NP framework for CER. Whereas Bayesian regression tree (BART) methodology is a useful tool for carrying out propensity score-based analysis, we want to consider situations in which one may not have full exchangeability of the data sets one is analyzing. We feel that the particular HDPM model we will use will provide better inferences in situations for which one can assume partial exchangeability.

The setting we describe in the proposal is not a meta-analysis of RCTs. Instead, we want to consider situations that might arise in CER. We feel that the appropriate comparator for CER is a Bayesian hierarchical regression-type model rather than standard approaches for meta-analysis.

Our model is a hierarchical version of a Dirichlet process (DP) mixture model. A DP places a probability distribution on the space of probability distributions. The posterior distribution with a DP prior is almost surely discrete, which can be awkward if one wants to characterize a continuous distribution. If, however, one assumes a DP prior for the distribution of a probability distribution's parameter(s), then the posterior distribution will be a mixture of these distributions centered at discrete locations, with the posterior DP as the mixing measure. E.g., if one assumes that the mean of a normal distribution has a DP prior, then the posterior will be a DP mixture of normals. The locations of this mixture will come from the DP posterior for the mean. In this way, the posterior can be thought of as a mixture of normals, even though the posterior is a DP mixture.

We will apply the propensity score adjustment approach to all studies and infer the treatment’s effect from all available data. The propensity score adjustment will be based on our proposed HDPM model approach. Roughly speaking, we will conduct a NP regression of the clinical outcomes on the propensity scores in a CER setting with the Bayesian NP method described in the proposal and in our response to the third comment. The Bayesian NP prior leads to a NP regression after integrating over the mixtures. Furthermore, our analyses will utilize the strength of the proposed HDPM models to allow information sharing among multiple randomized and/or observational studies.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/629">NCT00211133 - CR004414 (EPO-INT-76) DOUBLE BLIND - A Double-blind, Randomized, Placebo-controlled Study to Evaluate the Impact of Maintaining Hemoglobin Using Eprex (Epoetin Alfa) in Metastatic Breast Carcinoma Subjects Receiving Chemotherapy</a></li><li><a href="/node/630">NCT00270127 - CR005917 (EPO-INT-10) - Double-Blind, Placebo-Controlled Study to Assess the Effect of Early Intervention and/or Treatment With Epoetin Alfa on Anemia in Cancer Patients Receiving Non-Platinum-Containing Chemotherapy</a></li><li><a href="/node/631">NCT00270166 - CR005923 (EPO-INT-3) - A Placebo-Controlled Study on the Effect of Epoetin Alfa in Patients With Malignancy Receiving Chemotherapy</a></li><li><a href="/node/632">NCT00270049 - CR005905 (J89-040) - The Effect of Subcutaneous r-HuEPO in Patients With Chronic Lymphocytic Leukemia</a></li><li><a href="/node/633">NCT00270101 - CR005911 (EPO-INT-2) - A Placebo-Controlled Study on the Effect of r-huEPO in Patients With Multiple Myeloma Followed by an Open-Label Extension</a></li><li><a href="/node/634">NCT00270283 - CR006076 (I88-009) - A Double-Blind, Placebo-Controlled Study With Open-Label Follow-up to Determine the Safety and Efficacy of Subcutaneous Doses of r-HuEPO in AIDS Patients With Anemia Induced by Their Disease and AZT Therapy</a></li><li><a href="/node/635">NCT00091910 - CR004114 (EPO-ICU-002) - A Randomized, Double-Blind, Placebo-Controlled Study to Determine the Efficacy and Safety of Epoetin Alfa in Critically Ill Subjects</a></li><li><a href="/node/641">EPO-2 /// PR98-15-014 - Efficacy in the rHuEPO (Epoetin Alfa) in the Critically Ill Patient: A Randomized, Double Blind, Placebo-Controlled trial</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2017

2016-0912

Project Title: 
Reproduction analysis of the THERMOCOOL® SMARTTOUCH™ Catheter for the Treatment of Symptomatic Paroxysmal Atrial Fibrillation trial
Specific Aims of the Project: 

SPECIFIC AIM 1: To reproduce the results of the THERMOCOOL® SMARTTOUCH™ Catheter for the Treatment of Symptomatic Paroxysmal Atrial Fibrillation trial.
Our primary hypothesis is that we will be able to reproduce the results of the THERMOCOOL® SMARTTOUCH™ Catheter for the Treatment of Symptomatic Paroxysmal Atrial Fibrillation trial.

EXPLORATORY AIM 2: To identify the challenges and opportunities of reanalysis through the reproduction of results from the THERMOCOOL® SMARTTOUCH™ Catheter for the Treatment of Symptomatic Paroxysmal Atrial Fibrillation trial.
Because this aim is exploratory, we do not have a pre-specified hypothesis. Our primary objective from this aim will be to develop solutions that overcome potential challenges and accentuate opportunities for more effective reproducibility research in cardiovascular medicine.

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

Application Status

Complete
Scientific Abstract: 

Background: Reproduction of research findings has been identified as one potential area to reduce waste and improve efficiency in research.(1) Our team at the Cochrane Heart Group US Satellite at Northwestern University is interested in developing skills for reproducibility research to understand the challenges and opportunities to improve the process of research reproduction in cardiovascular medicine. The Yale Open Data Access (YODA) project includes one trial within cardiovascular medicine that is eligible for reproduction.

Objective: To conduct an independent reproduction and re-analysis of results from the THERMOCOOL® SMARTTOUCH™ Catheter for the Treatment of Symptomatic Paroxysmal Atrial Fibrillation trial (NCT01385202). Reproduction and re-analysis will be completed utilizing data collected through the YODA Project platform.

Study Design: Re-analysis of a randomized, controlled clinical trial.

Participants: Patients selected into and studied in the THERMOCOOL® SMARTTOUCH™ Catheter for the Treatment of Symptomatic Paroxysmal Atrial Fibrillation trial.

Main Outcome Measures: Arrhythmia free survival.

Statistical Analysis: We will evaluate the frequencies of baseline demographics and clinical/procedural characteristics of trial participants as mean (SD) or n (%). We will compare reported study results with our results using Fisher’s exact test, Chi-squared test, two-sample t-test, or Wilcoxon sign rank test, as appropriate. We will plot Kaplan-Meier survival curves along with the corresponding 95% confidence intervals based on the available data.

Brief Project Background and Statement of Project Significance: 

In January of 2015, the National Academy of Medicine (NAM) released a report titled Sharing Clinical Trial Data: Maximizing Benefits, Minimizing Risk.(2) One of the guiding principles described in this report was to increase public trust in clinical trials and promote sharing of trial data. There has recently been increasing recognition that the results of many studies (basic science, pre-clinical, and clinical research alike) published in high-profile journals cannot be reproduced or replicated, a dilemma labeled by some as the reproducibility crisis.(3) Similarly, reports have shown that there is little re-analysis conducted of the medical literature, and when re-analysis does occur, it is often completed by members of the original authorship and is therefore not independent.(4) Furthermore, re-analyses often have different findings than the original report; for instance a large systematic review of reanalysis trials found that 35% of published reanalysis papers came to different conclusions than the original paper.(4) This is a notable finding because reproducibility of results and independent verification are considered important pillars of the scientific method. In response, there has been a large push among many in the scientific community, including the NAM, to create more open and public access to large clinical trial data.

One of the main challenges in the open data initiative has been the lack of platforms to house and distribute data in an efficient and accessible manner. The Yale University Open Data Access (YODA) Project, along with their Data Holder partners, is focused on research transparency, which is an important step towards achieving the principles listed in the NAM report.(5) Our primary research focus will be re-analysis and reproduction of trial results requested through the YODA database. We aim to reproduce the results by explicitly following the methods laid out within the text of the article, as well as any online supplements, and to apply these methods to the data collected through the YODA database. The results of this exercise will be used to identify challenges and opportunities to reproducibility research in cardiovascular medicine, which is likely to become more important as more data become available. Our ultimate objective is to ensure greater confidence in trial results that lead to advances in research and improvements in human health.

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

We will include data from the THERMOCOOL® SMARTTOUCH™ Catheter for the Treatment of Symptomatic Paroxysmal Atrial Fibrillation trial (n=172 total participants; 161 participants in the safety cohort; 122 participants in the effectiveness cohort), which has been archived in YODA for this study.

Narrative Summary: 

Our primary research focus will be re-analysis and reproduction of the THERMOCOOL® SMARTTOUCH™ Catheter for the Treatment . We aim to reproduce the results by explicitly following the stated methods and to apply these to trial data collected through the YODA database. The results of this exercise will be used to identify challenges and opportunities to reproducibility research in cardiovascular medicine, which is likely to become more important as more data become available. Our ultimate objective is to ensure greater confidence in trial results that lead to advances in research and improvements in human health.

Project Timeline: 

Start Date: June 2016
Analysis Completion: August 2016
Manuscript Draft: October 2016
Submission: December 2016
Report to YODA: March 2017

Dissemination Plan: 

We propose submission of a brief report to a specialty journal with interest in research methodology, such as Circulation Cardiovascular Quality and Outcomes, Circulation
Research, JAMA Cardiology, or other similar journals.

Bibliography: 

1. Ioannidis JPA, Greenland S, Hlatky MA, Khoury MJ, Macleod MR, Moher D, et al. Increasing value and reducing waste in research design, conduct, and analysis. Lancet. 2014 Jan 11;383(9912):166–75. 
2. Committee on Strategies for Responsible Sharing of Clinical Trial Data, Board on Health Sciences Policy, Institute of Medicine. Sharing Clinical Trial Data: Maximizing Benefits, Minimizing Risk. Washington (DC): National Academies Press (US); 2015.
3. Begley CG, Ioannidis JP. Reproducibility in science: improving the standard for basic and preclinical research. Circ Res. 2015 Jan 2;116(1):116-26.
4. Ebrahim S, Sohani ZN, Montoya L, Agarwal A, Thorlund K, Mills EJ, Ioannidis JP. Reanalyses of randomized clinical trial data. JAMA. 2014 Sep 10;312(10):1024-32.
5. Doshi P, Vedula SS, Li T. YODA and truth seeking in medicine. BMJ. 2013;347:f4251.

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

Primary Outcome Measures:
1. The primary effectiveness endpoint for this study will be freedom from documented symptomatic atrial fibrillation (AF), atrial tachycardia (AT), or atrial flutter (AFL) episodes through 12-month follow-up (includes a three month blanking period).
2. Primary adverse events (AE) include death, myocardial infarction (MI), pulmonary vein (PV) stenosis, diaphragmatic paralysis, atrio-esophageal fistula, transient ischemic attack (TIA), stroke / cerebrovascular accident (CVA), thromboembolism, pericarditis, cardiac tamponade, pericardial effusion, pneumothorax, atrial perforation, vascular access complications, pulmonary edema, hospitalization (initial and prolonged), and heart block.
Secondary Outcome Measures:
Rate of acute success, defined as confirmation of entrance block in all pulmonary veins (PV).

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

This study describes the safety and effectiveness of an irrigated-tip ablation catheter without a comparator group. Therefore, our interest will be comparison between the primary and secondary outcomes reported by the study authors and by our team.

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

The following covariates were included in the primary study report, and we will report frequencies for each.
- Gender (male vs. female)
- Age (date of birth)
- Number of atrial fibrillation paroxysms in month preceding treatment
- Number of operators during treatment
- Previous treatment with antiarrhythmic drugs
- Prevalence of hypertension (SBP =>140)
- Prevalence of diabetes (FPG >= 126 or on Diabetic medications)
- Prevalence of coronary artery disease (medical history or self-report)
- Left atrial diameter (echocardiography)
- Left ventricular ejection fraction (echocardiography)
- Length of procedure time (minutes)
- Number and type of ablation (left superior pulmonary vein, right superior pulmonary vein, left inferior pulmonary vein, right inferior pulmonary vein, left common pulmonary vein)
- Prevalence of atrial flutter
- Adverse events (air or thrombotic embolism, acute PV stenosis, tamponade, phrenic nerve palsy, gastroparesis, catheter entrapment, major bleeding, or local hematoma requiring surgery)
- Induced atrial fibrillation
- Recurrence of atrial fibrillation (occurrence and length of time in days)

Statistical Analysis Plan: 

We will evaluate the frequencies of baseline demographics and clinical/procedural characteristics of trial participants as mean (SD) or n (%). We will compare reported study results with our results using Fisher’s exact test, Chi-squared test, two-sample t-test, or Wilcoxon sign rank test, as appropriate. We will plot Kaplan-Meier survival curves along with the corresponding 95% confidence intervals based on the available data.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/509">NCT01385202 - Smart-AF - THERMOCOOL® SMARTTOUCH™ Catheter for the Treatment of Symptomatic Paroxysmal Atrial Fibrillation</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2016
Associated Data: 
Results

2016-0903

Project Title: 
Use of TNF antagonist therapies with or without steroids for induction in Crohn’s disease: A Meta-analysis
Specific Aims of the Project: 

The primary aim of the project is to assess whether adjunctive therapy with corticosteroids results in greater efficacy than biological monotherapy. We have performed a systematic literature review and will now perform a meta-analysis of relevant data.

We hypothesize that patients with CD treated with anti-TNF agents will have a higher response rate if treated concomitantly with corticosteroids for induction of remission, without significant differences in the risk of adverse events.

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

Application Status

Ongoing
Scientific Abstract: 

Background: Biologic agents, such as TNF antagonists, and corticosteroids are highly effective for induction of remission in Crohn’s disease (CD) and ulcerative colitis (UC). Examination of data from a recent randomized controlled trial of combination therapy in patients with active CD (COMMIT) suggests that the addition of corticosteroids to infliximab may result in higher remission rates. However this possibility has not been evaluated by a dedicated RCT.
Objective: perform a meta-analysis of existing induction trials of biologic therapies to assess whether adjunctive therapy with corticosteroids results in greater efficacy than biological monotherapy. A secondary objective is to compare the safety of the two strategies.
Study Design: Meta-analysis of randomized control studies (RCTs) that fit search criteria
Participants: Study subjects in previously performed RCTs, age>18 years
Main Outcome Measure(s): The primary analysis will be a pooled summary estimate of clinical remission on anti-TNF therapy stratified by corticosteroid exposure at baseline. Secondary outcome to be measured are luminal response and safety of the two strategies.
Statistical Analysis: Standard meta-analysis methods will be used. The test of heterogeneity will be performed using the chi-squared test and the I2 test. Stratified analyses and meta-regression will be performed to explore factors that may explain heterogeneity between studies. This includes potential confounding factors such as disease severity (ex. Higher CDAI, CRP levels) or concurrent immunomodulator use.

Brief Project Background and Statement of Project Significance: 

Biologic agents, such as TNF antagonists, and corticosteroids are highly effective for induction of remission in Crohn’s disease (CD) and ulcerative colitis (UC). Examination of data from a recent randomized controlled trial of combination therapy in patients with active CD (COMMIT) suggests that the addition of corticosteroids to infliximab may result in higher remission rates. However this possibility has not been evaluated by a dedicated RCT. Our objective is to perform a meta-analysis of existing induction trials of biologic therapies to assess whether adjunctive therapy with corticosteroids results in greater efficacy than biological monotherapy. A secondary objective is to compare the safety of the two strategies.

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

A systemic review was performed of Medline, Central and Embase for all English language studies in adult patients with Crohn’s disease studying the efficacy of either adalimumab, certolizumab pegol, golimumab, and infliximab. Abstracts were included in the search in addition to a hand search to identify randomized controlled trials (manuscripts and abstracts).

Inclusion criteria:
• Randomized, placebo-controlled trials in which patient level data (case report forms) is obtainable and information regarding length and dosing of concurrent steroids
• Adult patients with Crohn’s disease
• Anti-TNF agent used includes any of Infliximab, adalimumab, certolizumab pegol, and golimumab
• Duration of 4-12 weeks for induction
• Data available for remission rates at a short time interval (i.e. between 4-12 weeks)

We have identified 16 full text articles that are eligible for our study. In order to meet the aim of our study, non-published patient level data must be obtained.

Narrative Summary: 

Biologic agents, such as TNF antagonists, and corticosteroids are highly effective for induction of remission in Crohn’s disease (CD) and ulcerative colitis (UC). Examination of data from a recent randomized controlled trial of combination therapy in patients with active CD (COMMIT) suggests that the addition of corticosteroids to infliximab may result in higher remission rates. However this possibility has not been evaluated by a dedicated RCT. Our objective is to perform a meta-analysis of existing induction trials of biologic therapies to assess whether adjunctive therapy with corticosteroids results in greater efficacy than biological monotherapy.

Project Timeline: 

We have already performed a systematic literature review and have identified randomized control trials that fill our inclusion criteria. We have extracted all relevant published data but much data is still missing. Once access to study data is granted, we anticipate data extraction and analysis to take no longer than 2 months. Manuscript drafting will take another 1 month. Manuscript submission is aimed to take place no later than September 2016.

Dissemination Plan: 

The results of this meta-analysis is anticipated to be of great interest to all IBD clinicians. A manuscript will be submitted about 3 months after data acquisition. As such, we plan to submit our manuscript to Gastroenterology, American Journal of Gastroenterology or Clinical Gastroenterology and Hepatology.

Bibliography: 

Colombel JF, Sandborn WJ, Reinisch W, Mantzaris GJ, Kornbluth A, Rachmilewitz D, Lichtiger S, D'Haens G, Diamond RH, Broussard DL, Tang KL, van der Woude CJ, Rutgeerts P; SONIC Study Group. Infliximab, azathioprine, or combination therapy for Crohn's disease. N Engl J Med. 2010 Apr 15;362(15):1383-95.

Colombel JF, Sandborn WJ, Rutgeerts P, Enns R, Hanauer SB, Panaccione R, Schreiber S, Byczkowski D, Li J, Kent JD, Pollack PF. Adalimumab for maintenance of clinical response and remission in patients with Crohn's disease: the CHARM trial. Gastroenterology. 2007 Jan;132(1):52-65.

Dewint P, Hansen BE, Verhey E, Oldenburg B, Hommes DW, Pierik M, Ponsioen CI, van Dullemen HM, Russel M, van Bodegraven AA, van der Woude CJ. Adalimumab combined with ciprofloxacin is superior to adalimumab monotherapy in perianal fistula closure in Crohn's disease: a randomised, double-blind, placebo controlled trial (ADAFI). Gut. 2014 Feb;63(2):292-9.

Farrell RJ, Alsahli M, Jeen YT, Falchuk KR, Peppercorn MA, Michetti P. Intravenous hydrocortisone premedication reduces antibodies to infliximab in Crohn's disease: a randomized controlled trial. Gastroenterology. 2003 Apr;124(4):917-24
Feagan BG, Coteur G, Tan S, Keininger DL, Schreiber S. Clinically meaningful improvement in health-related quality of life in a randomized controlled trial of certolizumab pegol maintenance therapy for Crohn's disease. Am J Gastroenterol. 2009 Aug;104(8):1976-83.

Feagan BG, McDonald JW, Panaccione R, Enns RA, Bernstein CN, Ponich TP, Bourdages R, Macintosh DG, Dallaire C, Cohen A, Fedorak RN, Paré P, Bitton A, Saibil F, Anderson F15, Donner A16, Wong CJ2, Zou G, Vandervoort MK, Hopkins M, Greenberg GR. Methotrexate in combination with infliximab is no more effective than infliximab alone in patients with Crohn's disease. Gastroenterology. 2014 Mar;146(3):681-688.

Hanauer SB, Feagan BG, Lichtenstein GR, Mayer LF, Schreiber S, Colombel JF, Rachmilewitz D, Wolf DC, Olson A, Bao W, Rutgeerts P; ACCENT I Study Group. Maintenance infliximab for Crohn's disease: the ACCENT I randomised trial. Lancet. 2002 May 4;359(9317):1541-9.

Hanauer SB, Sandborn WJ, Rutgeerts P, Fedorak RN, Lukas M, MacIntosh D, Panaccione R, Wolf D, Pollack P. Human anti-tumor necrosis factor monoclonal antibody (adalimumab) in Crohn's disease: the CLASSIC-I trial. Gastroenterology. 2006 Feb;130(2):323-33.

Present DH, Rutgeerts P, Targan S, Hanauer SB, Mayer L, van Hogezand RA, Podolsky DK, Sands BE, Braakman T, DeWoody KL, Schaible TF, van Deventer SJ. Infliximab for the treatment of fistulas in patients with Crohn's disease. N Engl J Med. 1999 May 6;340(18):1398-405.

Rutgeerts P, Van Assche G, Sandborn WJ, Wolf DC, Geboes K, Colombel JF, Reinisch W; EXTEND Investigators, Kumar A, Lazar A, Camez A, Lomax KG, Pollack PF, D'Haens G. Adalimumab induces and maintains mucosal healing in patients with Crohn's disease: data from the EXTEND trial. Gastroenterology. 2012 May;142(5):1102-1111.

Sandborn WJ, Rutgeerts P, Enns R, Hanauer SB, Colombel JF, Panaccione R, D'Haens G, Li J, Rosenfeld MR, Kent JD, Pollack PF. Adalimumab induction therapy for Crohn disease previously treated with infliximab: a randomized trial. Ann Intern Med. 2007 Jun 19;146(12):829-38.

Sandborn WJ, Abreu MT, D'Haens G, Colombel JF, Vermeire S, Mitchev K, Jamoul C, Fedorak RN, Spehlmann ME, Wolf DC, Lee S, Rutgeerts P. Certolizumab pegol in patients with moderate to severe Crohn's disease and secondary failure to infliximab. Clin Gastroenterol Hepatol. 2010 Aug;8(8):688-695.

Sandborn WJ, Feagan BG, Stoinov S, Honiball PJ, Rutgeerts P, Mason D, Bloomfield R, Schreiber S; PRECISE 1 Study Investigators Certolizumab pegol for the treatment of Crohn's disease. N Engl J Med. 2007 Jul 19;357(3):228-38.

Sands BE, Blank MA, Patel K, van Deventer SJ; ACCENT II Study. Long-term treatment of rectovaginal fistulas in Crohn's disease: response to infliximab in the ACCENT II Study. Clin Gastroenterol Hepatol. 2004 Oct;2(10):912-20.

Schreiber S, Khaliq-Kareemi M, Lawrance IC, Thomsen OØ, Hanauer SB, McColm J, Bloomfield R, Sandborn WJ; PRECISE 2 Study Investigators. Maintenance therapy with certolizumab pegol for Crohn's disease. N Engl J Med. 2007 Jul 19;357(3):239-50.

Schreiber S, Rutgeerts P, Fedorak RN, Khaliq-Kareemi M, Kamm MA, Boivin M, Bernstein CN, Staun M, Thomsen OØ, Innes A; CDP870 Crohn's Disease Study Group. A randomized, placebo-controlled trial of certolizumab pegol (CDP870) for treatment of Crohn's disease. Gastroenterology. 2005 Sep;129(3):807-18.

Watanabe M, Hibi T, Lomax KG, Paulson SK, Chao J, Alam MS, Camez A; Study Investigators. Adalimumab for the induction and maintenance of clinical remission in Japanese patients with Crohn's disease. J Crohns Colitis. 2012 Mar;6(2):160-73.

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
Summary-level data meta-analysis will pool data from YODA Project with other additional data sources
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 analysis will be a pooled summary estimate of clinical remission on anti-TNF therapy stratified by corticosteroid exposure at baseline. Patients with high dose concurrent steroid use (as defined in the inclusion criteria) will be analyzed separately from those with low concurrent steroid use. In this main analysis, all anti-TNF agents will be combined and will be evaluated based on remission at induction (between 4-12 weeks).

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

Many patients are treated with a weaning course of corticosteroids at the time of induction, so those patients who received the equivalent of >20mg daily of prednisone or equivalent during the first four weeks of induction treatment will be analyzed in the concomitant high-dose steroid therapy group. Those treated with <20mg daily of prednisone or equivalent during the first four weeks of induction will be analyzed in the concomitant low-dose steroid therapy group. Those who received no concurrent steroids will be analyzed in the no steroid group. Data will also be collected on patients in the placebo arms who received concomitant high dose steroids, low dose steroids, or no steroids, as per the definitions above.

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

A secondary outcome that will be evaluated is luminal response. Response is measured differently between studies, but most commonly for CD will be based on a decrease in the Crohn’s disease activity index (CDAI). Studies that report outcomes at weeks 4-12 will be combined irrespective of the duration of the induction period. To account for differences in steroid use between studies (i.e forced tapering, fixed dose, etc), the average daily dose of steroids used during the first four weeks of induction will be used to classify patients. An average daily dose of 20mg or more of prednisone will be considered high concurrent corticosteroid use. An average dose of less than 20mg of prednisone per day will be considered low dose. Safety will also be evaluated based on the same stratification and will include infusion/injection site reactions, malignancy, infections, and death.

Statistical Analysis Plan: 

Statistical analyses will be performed by a PhD biostatistician, Dr. Emilia Bagiella (Mount Sinai, NY). The main analysis will be based on the intention-to-treat population from each study. The test of heterogeneity will be performed using the chi-squared test and the I2 test. The I2 test describes the percentage of variability in effect estimates that is due to heterogeneity rather than chance, wherein an I2 test greater than 50% suggests significant heterogeneity. A random effects model will be used assuming that heterogeneity will exist between studies; if the study data meet the criteria for homogeneity then a fixed effects model will be used. Stratified analyses and meta-regression will be performed to explore factors that may explain heterogeneity between studies. This includes potential confounding factors such as disease severity (ex. Higher CDAI, CRP levels) or concurrent immunomodulator use. Publication bias will be assessed using a funnel plot.

Sensitivity analyses (analyses to be run removing certain studies to see how it affects the results)
• Other sensitivity analyses may be run if there are outlier studies with unique design characteristics that appear to have a strong influence on the results (standard according to the Cochrane Handbook, section 9.7)

Subgroup analyses
• No concurrent immunomodulator versus immunomodulator-treated patients
• Individual anti-TNF agents will be compared with or without steroids, but not against each other

In addition to the 4 studies requested from YODA, data from for the following 12 studies have been requested from AbbVie, UCB, and Dr. Farrell. Of note, IPD will not be pooled within the data sharing platform.

1. Farrell, R. J., et al. (2003). "Intravenous hydrocortisone premedication reduces antibodies to infliximab in Crohn's disease: A randomized controlled trial." Gastroenterology 124(4): 917-924.
2. Colombel, J., et al. (2007). "Adalimumab for Maintenance of Clinical Response and Remission in Patients With Crohn's Disease: The CHARM Trial." Gastroenterology 132(1): 52-65.
3. Dewint, P., et al. (2014). "Adalimumab combined with ciprofloxacin is superior to adalimumab monotherapy in perianal fistula closure in Crohn's disease: A randomised, double-blind, placebo controlled trial (ADAFI)." Gut 63(2): 292-299.
4. Hanauer, S. B., et al. (2006). "Human anti-tumor necrosis factor monoclonal antibody (adalimumab) in Crohn's disease: The CLASSIC-I trial." Gastroenterology 130(2): 323-332.
5. Rutgeerts, P., et al. (2012). "Adalimumab induces and maintains mucosal healing in patients with Crohn's Disease: Data from the EXTEND trial." Gastroenterology 142(5): 1102-1111.
6. Sandborn, W. J., et al. (2007). "Adalimumab induction therapy for Crohn disease previously treated with infliximab: a randomized trial.” Annals of internal medicine 146(12): 829-838.
7. Watanabe, M., et al. (2012). "Adalimumab for the induction and maintenance of clinical remission in Japanese patients with Crohn's disease." Journal of Crohn's & colitis 6(2): 160-173.
8. Sandborn, W. J., et al. (2007). "Certolizumab pegol for the treatment of Crohn's disease." New England journal of medicine 357(3): 228-238.
9. Sandborn, W. J., et al. (2010). "Certolizumab pegol in patients with moderate to severe Crohn's disease and secondary failure to infliximab." Clinical Gastroenterology & Hepatology 8(8): 688-695.
10. Sandborn, W. J., et al. (2011). "Certolizumab Pegol for Active Crohn's Disease: A Placebo-Controlled, Randomized Trial." Clinical gastroenterology and hepatology 9(8): 670-678.
11. Schreiber, S., et al. (2005). “A randomized, placebo-controlled trial of certolizumab pegol (CDP870) for treatment of Crohn's disease.” Gastroenterology 129, 807-818.
12. Schreiber, S., et al. (2007). “Maintenance therapy with certolizumab pegol for Crohn's disease.” New England journal of medicine 357, 239-250.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/159">NCT00094458 - C0168T67 - Multicenter, Randomized, Double-Blind, Active Controlled Trial Comparing REMICADE® (infliximab) and REMICADE plus Azathioprine to Azathioprine in the Treatment of Patients with Crohn’s Disease Naive to both Immunomodulators and Biologic Therapy (Study of Biologic and Immunomodulator Naive Patients in Crohn’s Disease)</a></li><li><a href="/node/353">NCT00207662 - C0168T21 - ACCENT I - A Randomized, Double-blind, Placebo-controlled Trial of Anti-TNFa Chimeric Monoclonal Antibody (Infliximab, Remicade) in the Long-term Treatment of Patients With Moderately to Severely Active Crohn's Disease</a></li><li><a href="/node/354">NCT00207766 - C0168T26 - ACCENT II - A Randomized, Double-blind, Placebo-controlled Trial of Anti-TNF Chimeric Monoclonal Antibody (Infliximab, Remicade) in the Long Term Treatment of Patients With Fistulizing CROHN'S Disease</a></li><li><a href="/node/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></ol>
Make Publicly Available : 
Year of Data Access: 
2016

2016-0880

Project Title: 
Incidence of death and other SAEs related to second generation antipsychotic or placebo treatment in RCTs - a systematic review and meta-analysis
Specific Aims of the Project: 

The aims of this project are to examine whether antipsychotic drugs increase the risk for death and other serious adverse events, to further evaluate the differential incidence of specific serious adverse events in pharmacologically treated (SGA-drug-group) and non treated (placebo-group) patients suffering from mental disorders and to find out which patient or treatment related factor are associated with their occurence.

Main hypothesis (two sided ): There is an overall significant difference in mortality and incidence of serious adverse events in antipsychotic drug trials between the verum and the placebo group.

The same two sided hypothesis will be used for all identified specific serious adverse events.

Subgroup analysis will incude antipsychotic drug used, diagnostic subgroup, age, gender, drug combination, dosage.
We expect the state of treatment (active antipsychotic treatment or placebo) to be the main predictor.
Other potential predictors that will be adressed in subgroup analysis are specific antipsychotic drug, diagnostic subgroup, age, gender, combination of drugs, dosage.

Please find attached our successfull application to the German ministry of education and research (BMBF) and the PROSPERO protocol for further information.

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

Application Status

Complete
Scientific Abstract: 

Background: Antipsychotic drugs are the mainstay for the treatment of schizophrenia, but are also very important and widely used therapeutic options for the treatment of several other psychiatric diseases and disorders. In the community of patients, professionals and scientists it is currently a highly discussed topic, if their application is associated with a decreased or increased risk of serious adverse events including death. Up to date analysis are based on observational studies with inherent methodological problems.
Study Design: We are working on a systematic review including a large meta-analysis of randomized controlled trials to evaluate and differentiate the risk of death and other serious adverse events related to antipsychotic drug treatment or no treatment of mental dsorders.
Participants: Participants of randomized placebo-controlled trials of second generation antipsychotic drugs irrespective of indication, age or gender.
Main Outcome Measures: incidence rates of serious adverse events including death.
Statistical Analysis: Odds ratios and their 95% confidence intervals will be calculated and combined in pairwise meta-analysis.

Please find attached our successfull application to the German ministry of education and research (BMBF) and the PROSPERO protocol for further information.

Brief Project Background and Statement of Project Significance: 

Antipsychotic drugs are the mainstay for the treatment of schizophrenia, but are also very important and widely used therapeutic options for the treatment of several other psychiatric diseases and disorders. In the community of patients, professionals and scientists it is currently a highly discussed topic, if their application is associated with a decreased or increased risk of serious adverse events including death.
Up to date analysis are based on observational studies with inherent methodological problems.
To reach a high level of precision and confidence about these rare but very important outcomes, analyses on the basis of randomized populations and high number of participants are required. We are planning to achieve this goal by conducting by a big size meta-analyses of RCT comparing SGAs as a groups to placebo over all indications.

Please find attached our successfull application for a grant from the German ministry of education and research (BMBF) for further details concerning the relevance of our project and references to prior work.

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

Systematic review and meta-analysis:

Search strategy: We ran electronic searches in the databases MEDLINE, EMBASE, Cochrane Central Register of Randomised Trials (CENTRAL), BIOSIS, CINAHL, Dissertation Abstracts, LILACS, PSYNDEX, PsycINFO). Additionally we contacted all SGA-marketing pharma companies for missing relevant data.

inclusion criteria: Participants of randomized placebo-controlled clinical trials of second generation antipsychotic drugs irrespective of underlying mental disease, age or gender.

Included SGAs are amisulpride, aripiprazole, asenapine, brexpiprazole, cariprazine, clozapine, iloperidone, lurasidone, olanzapine, paliperidone, quetiapine, risperidone, sertindole, ziprasidone and zotepine. First generation antipsychotics will only be evaluated if they are used as additional active comparator.

Please find attached our successfull application to the German ministry of education and research (BMBF) and the PROSPERO protocol for further information.

Narrative Summary: 

Antipsychotic drugs are the mainstay for the treatment of schizophrenia, but are also very important and widely used therapeutic options for the treatment of several other psychiatric diseases and disorders. In the community of patients, professionals and scientists it is currently a highly discussed topic, if their application is associated with a decreased or increased risk of serious side effects including death.
Since these serious outcomes are fortunately rare, this can only be answered by analyses including high numbers of patients. We are tackling this issue therefore by a big size meta-analysis comparing antipsychotic drugs as a group to placebo in RCTs over all indications.

Project Timeline: 

Start of project: 09/2015
First contact of data holders: 11/2015
Actual state of the project: identification of included RCTs from literature search and data extraction.

It is planned to finish data extraction and to start data analysis by 07/2016
First data presentations and publications are planned for the following month.

According to the framework of our grant, there is a deadline for data presentation in 03/2017.

Dissemination Plan: 

We will produce a very large review with approximately 50000 participants. The research question is a priority for patients with schizophrenia and it is important for many other psychiatric patient groups for which antipsychotics have indications or are used “off-label”. Therefore, it is likely that we will be able to publish the results in a general medicine journal with high visibility such as the BMJ or the Lancet in which other reviews of our group have already been published [29, 30, 31, 32]. It can be expected that our findings will be rapidly implemented in national and international treatment guidelines. For example, Stefan Leucht is a member of the group producing the schizophrenia and depression guidelines of the German national psychiatric association (DGPPN) and of the British Association of Psychopharmacology, and he is leading the schizophrenia guideline group of the Collegium Internationale Psychopharmacologicum (CINP).
For references and further information please find attached our successfull application for a grant from the german ministry of education.

Bibliography: 

For references and further information please find attached our successfull application for a grant from the german ministry of education.

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

The main outcome measure is the incidence of serious adverse events in the drug group and the placebo group.
The effect size measure will be the odds ratio (OR) and its 95% confidence intervals (CIs). We will calculate the number needed to treat to provide benefit /to induce harm, and its 95% confidence intervals (CIs). Analyses will be carried out in accordance to the ‘intention-to-treat’ principal when possible (‘once randomized always analyze’). As all outcomes of interest (serious adverse events) are rare we will assume for those who have been lost to follow-up that they will not have had the outcome (unless it occurred before dropping out), because other strategies would overestimate the risk because the outcomes are rare.

If data is available, we will calculate incidence rates per mean time in study to control for different periods of observation in the drug and the placebo group.

Please find attached our successfull application to the German ministry of education and research (BMBF) and the PROSPERO protocol for further information.

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

We expect the state of treatment (active antipsychotic treatment or placebo) to be the main predictor.

Please find attached our successfull application to the German ministry of education and research (BMBF) and the PROSPERO protocol for further information.

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

Other potential predictors that will be adressed in subgroup analysis are specific antipsychotic drug, diagnostic subgroup, age, gender, combination of drugs, dosage.

Please find attached our successfull application to the German ministry of education and research (BMBF) and the PROSPERO protocol for further information.

Statistical Analysis Plan: 

Pairwise meta-analyses within a Bayesian framework will be used to estimate the summary comparative effect sizes. All outcomes will be dichotomous and be primarily analysed as odds ratios, supplemented by NNT/NNH. Special statistical attention in terms of data synthesis and heterogeneity assessment will be paid to the fact that events will be rare. Predefined subgroups analyses will address: diagnostic subgroup, age, gender, antipsychotic drug used, antipsychotic combinations, dose. Publication bias will be examined with funnel-plot methods, recommendations will be made with GRADE.
The following sensitivity analyses of the primary outcome are planned a priori: a) random-effects instead of fixed effects model, b) exclusion of open RCTs, c) exclusion of studies that used doses higher than in the official labels (“off-label doses”).

Please find attached our successfull application to the German ministry of education and research (BMBF) and the PROSPERO protocol for further information.

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/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/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/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/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/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/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/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/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/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/850">NCT00249158 - RIS-AUS-5/CR006010 - Risperidone in the Treatment of Behavioural and Psychological Signs and Symptoms in Dementia (BPSSD): a Multicentre, Double-blind, Placebo-controlled Parallel-group Trial</a></li><li><a href="/node/851">RIS-BEL-14 - Risperidone in the treatment of behavioural disturbances in patients with Alzheimer's dementia: a double-blind placebo-controlled trial</a></li><li><a href="/node/852">NCT00261508 - RIS-CAN-23/CR006106 - Efficacy And Safety Of Risperidone In The Treatment Of Children With Autistic Disorder And Other Pervasive Developmental Disorders: A Canadian, Multicenter, Double-Blind, Placebo-Controlled Study</a></li><li><a href="/node/853">NCT00249236 - RIS-IND-2/CR006064 - The Efficacy And Safety Of Flexible Dosage Ranges Of Risperidone Versus Placebo In The Treatment Of Manic Or Mixed Episodes Associated With Bipolar I Disorder</a></li><li><a href="/node/854">NCT00249145 - RIS-INT-24/CR006046 - Risperidone in the Treatment of Behavioral Disturbances in Demented Patients: an International, Multicenter, Placebo-controlled, Double-blind, Parallel-group Trial Using Haloperidol as Internal Reference</a></li><li><a href="/node/855">NCT00250367 - RIS-INT-46/CR006058 - The Safety And Efficacy Of Risperdal (Risperidone) Versus Placebo As Add-On Therapy To Mood Stabilizers In The Treatment Of The Manic Phase Of Bipolar Disorder</a></li><li><a href="/node/856">RIS-INT-83 - Efficacy and safety of a flexible dose of risperidone versus placebo in the treatment of psychosis of Alzheimer’s disease. 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Make Publicly Available : 
Year of Data Access: 
2016
Associated Data: 
Results

2016-0767

Project Title: 
Determinants of treatment outcomes of multidrug-resistant tuberculosis (MDR-TB): an Individual Patient Data (IPD) Meta-Analysis - Update
Specific Aims of the Project: 

Conduct an updated individual patient data (IPD) meta-analysis of patients who were treated for MDR-TB to determine treatment correlates with treatment outcomes.
We will have a particular focus on certain drugs, for which we believe there is significant new evidence from recently published studies:
• High-dose isoniazid
• Later generation fluoroquinolone
• Linezolid
• Clofazimine
• Bedaquiline
• Delamanid
• Carbapenems

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

Application Status

Incomplete
Scientific Abstract: 

Background: Multidrug-resistant tuberculosis (MDR-TB) infection is associated with long treatment duration, use of toxic drugs, and generally poor outcomes. Individual patient data meta-analysis can be used to identify the treatment correlates of successful outcomes of MDR-TB, and update the current recommendation for MDR-TB treatment.
Objective: Conduct an updated individual patient data (IPD) meta-analysis of patients who were treated for MDR-TB to determine treatment correlates with treatment outcomes.
Study Design: Systematic review has been performed to identify the studies that reported MDR-TB treatment regimens and outcomes since January 2009. Study authors will be contacted to ask for sharing individual patient data for each study. All datasets from different centers will be assembled into one database, and data will be meta-analyzed to identify determinants of treatment outcomes of multidrug-resistant tuberculosis.
Participants: Microbiologically confirmed MDR-TB patients reported in eligible studies published since January 2009 in peer-reviewed / indexed journals
Main Outcome Measures: end-of-treatment outcomes (cure, treatment completed, death, treatment default, treatment failure, transfer out & relapse); time to sputum culture conversion; adverse events.
Statistical Analysis: Individual patient data meta-analysis using random effects model

Brief Project Background and Statement of Project Significance: 

Multidrug-resistant tuberculosis (MDR-TB), defined as tuberculosis resistant to at least isoniazid (INH) and rifampin (RIF), has become a major threat to global tuberculosis control in recent years. MDR-TB requires prolonged treatment with a combination of multiple second line drugs which are more toxic and yet less effective than first line drugs. Treatment outcomes are poor and there is considerable controversy regarding the best treatment regimen – largely because of inadequate evidence to support current treatment recommendations.
In 2009, WHO issued a call for evidence to draft new recommendations for treatment of MDR-TB. Initially, 3 systematic reviews were completed and meta-analyzed the data of eligible literatures [1-3]. However, identification of optimal treatment regimens was extremely difficult as pooling of results across studies was limited by the complexity of the patients’ characteristics and individualized treatment. To address this problem, we (the McGill group) conducted an individual patient data (IPD) study in 2010, and a data base was assembled of individual patient records from almost 10,000 MDR-TB patients from 32 centers of 20 countries [4].
This rich data base has allowed seven distinct set of analyses to address different important questions regarding interpretation of DST, role of surgery, prognosis and correlates of treatment success of MDR-TB. Meta-analysis of this data set produced the majority of evidence which the WHO expert committee used to make new recommendations for MDR-TB treatment published in 2011 [5]. Five papers (one in PLoS Medicine, two in ERJ, and two in CID) have been published so far and the results have influenced several sets of WHO recommendations [4, 6-10].
In the past five years, newer TB drugs has been used to treat MDR-TB patients, including later generation fluoroquinolones, linezolid, clofazimine, delamanid, and bedaquiline. US Centers for Disease Control and Prevention (CDC), American Thoracic Society (ATS) and Infectious Diseases Society of America (IDSA) plan to issue revised guidelines for treatment of MDR-TB and other forms of drug resistant TB in the fall of 2016. They approached us at McGill to update the IPD in MDR-TB with a particular emphasis on these newer drugs. Therefore, we (the McGill group), in collaboration with ATS/CDC/IDSA, will conduct an updated IPD study in order to generate evidence to update the current MDR-TB treatment recommendations. Authors of eligible publications from Jan. 2009 will be contacted and asked for participation in this study. An updated data base will be assembled and individual patient data will be analyzed to address major questions regarding MDR-TB treatment.

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

Data source of the study:
Individual patient data from eligible studies that reported MDR-TB treatment regimens and outcomes, published since January 2009 in peer-reviewed / indexed journals
Inclusion criteria:
1) Studies published since January 2009 in peer-reviewed / indexed journals. A systematic review has been conducted by investigators at McGill University to identify the eligible studies.
2) MDR-TB is bacteriologic confirmed (phenotypic or genotypic)
3) Patients with pulmonary MDR-TB. Extra pulmonary MDR-TB patients could also be included and this could be a secondary objective for later analysis.
4) Adults (age definition may vary but at minimum age ≥ 12)
5) The study reported at least 25 patients with MDR-TB
6) The study must report at least one of the following outcomes: end-of-treatment outcomes, time to culture conversion, or adverse events.
Exclusion criteria:
1) Studies published only as grey literature will be excluded
2) Studies describing the results of treatment with regimens of less than 12 months will not be included

Narrative Summary: 

Multidrug-resistant tuberculosis (MDR-TB) infection is associated with long treatment duration, use of toxic drugs, and generally poor outcomes. Newer TB drugs has been used in recent years to treat MDR-TB. In order to update the evidence for treatment recommendations of MDR-TB, we are planning to obtain and meta-analyze individual patient data from all recent MDR-TB studies, and identify the treatment correlates of successful outcomes of MDR-TB.

Project Timeline: 

Project start date: January, 2016 (already started)
All datasets from different participating centers collected (including the two RCT data requested through YODA project): By the end of April, 2016
Assemble all datasets from different centers into one data base for analysis: by May, 2016
Preliminary analysis completion date: by August, 2016
Final analysis completion date: by February, 2017
Date manuscript drafted and first submitted for publication: by August, 2017
Date results reported back to the YODA Project: at the same time when first paper is submitted for publication (extension of the agreement may be needed).

Dissemination Plan: 

Anticipated products:
• ATS/CDC/IDSA updated treatment guideline for MDR-TB
• Possibly updated WHO guideline for MDR-TB treatment
• Publications in peer-reviewed journals
Potential suitable journals for submission:
• Clinical Infectious Diseases
• European Respiratory Journal
• PLOS Medicine
Target audiences:
• Health professionals and researchers involved in MDR-TB treatment

Bibliography: 

1. Akcakir Y. Correlates of treatment outcomes of multidrug-resistant tuberculosis (MDR-TB): a systematic review and meta-analysis. [PhD dissertation]. Montreal: Department of Epidemiology & Biostatistics, McGill University. 2010.
2. Johnston JC, Shahid NC, Sadatsafavi M, Fitzgerald, JM. Treatment outcomes of multidrug-resistant tuberculosis: a systematic review and meta-analysis. PloS one 2009, 4(9): e6914.
3. Orenstein EW, Basu S, Shah NS, et al. Treatment outcomes among patients with multidrug-resistant tuberculosis: systematic review and meta-analysis. The Lancet infectious diseases 2009, 9(3): 153-161.
4. Ahuja SD, Ashkin D, Avendano M, et al. Multidrug resistant pulmonary tuberculosis treatment regimens and patient outcomes: an individual patient data meta-analysis of 9,153 patients. PLoS medicine 2012, 9(8): 1212.
5. World Health Organization. Guidelines for the programmatic management of drug-resistant tuberculosis-2011 update. World Health Organization, 2011.
6. Falzon D, Gandhi N, Migliori GB, et al. Resistance to fluoroquinolones and second-line injectable drugs: impact on MDR-TB outcomes. European Respiratory Journal 2012, erj01347-02012.
7. Migliori GB, Sotgiu G, Neel RG, et al. Drug resistance beyond extensively drug-resistant tuberculosis: individual patient data meta-analysis. European Respiratory Journal 2013, 42(1): 169-179.
8. Bastos ML, Hussain H, Weyer K, et al. Treatment outcomes of patients with multidrug-resistant and extensively drug-resistant tuberculosis according to drug susceptibility testing to first-and second-line drugs: an individual patient data meta-analysis. Clinical Infectious Diseases 2014, 59(10): 1364-1374.
9. Gregory JF, Mitnick CD, Benedetti A, et al. Surgery as an adjunctive treatment for multi-drug resistant tuberculosis: an individual patient data meta-analysis. Clinical Infectious Diseases 2016, ciw002.
10. World Health Organization. Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis. World Health Organization, 2014.
11. Laserson KF, Thorpe LE, Leimane V, et al. Speaking the same language: treatment outcome definitions for multidrug-resistant tuberculosis. The International Journal of Tuberculosis and Lung Disease 2005, 9(6): 640-645.
12. Schabenberger O. Introducing the GLIMMIX procedure for generalized linear mixed models. SUGI 30 Proceedings 2005, 196-130.
13. Turner RM, Omar RZ, Yang M, et al. A multilevel model framework for meta-analysis of clinical trials with binary outcomes. Statistics in medicine 2000, 19(24): 3417-3432.
14. Thompson SG, Turner RM, Warn DE. Multilevel models for meta-analysis, and their application to absolute risk differences. Statistical Methods in Medical Research 2001, 10(6): 375-392.
15. Higgins J, Thompson SG. Quantifying heterogeneity in a meta‐analysis. Statistics in medicine 2002, 21(11): 1539-1558.
16. Pinheiro JC, Bates DM. Approximations to the log-likelihood function in the nonlinear mixed-effects model. Journal of computational and Graphical Statistics 1995, 4(1): 12-35.
17. Williamson E, Morley R, Lucas A, Carpenter J. Propensity scores: from naive enthusiasm to intuitive understanding. Statistical methods in medical research 2012, 21(3): 273-293.

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
Summary-level data meta-analysis:
Summary-level data meta-analysis will pool data from YODA Project with other additional data sources
Participant-level data meta-analysis:
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 assessed will be:
1) End-of-treatment outcomes: Cure, Treatment complete, Death, Treatment default, Treatment failure, Transfer out (& relapse if measured). These will be defined by Laserson et al., 2005 [11].
Additional outcomes assessed will be:
2) Time to culture conversion, defined as the months/days that a patient achieved sputum conversion from the start of the treatment.
3) Adverse events (AEs). We are particular interested in serious AE that is classified as grade 3-4 or leads to drug permanently discontinued. Identifying the drug responsible for AEs is also important for the analysis.

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

Treatment regimens for MDR-TB:
1) Drugs used in intensive phase (drug names and dosage)
2) Duration of intensive phase
3) Drugs used in continuation phase (drug names and dosage)
4) Duration of continuation phase
5) Surgery: whether surgery was performed for MDR-TB treatment, and when surgery was performed

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

Demographic and clinical information at diagnosis:
1) Age: the age of MDR-TB patient
2) Gender: male or female
3) Height, weight and BMI
4) Smoking: current smoker, ex-smoker, non-smoker or unknown.
5) HIV status: whether the patient was HIV positive
6) ART information: if the patient was HIV positive, whether he/she received antiretroviral therapy
7) Diabetes: whether the patient had diabetes
8) Prior first-line TB treatment history: whether the patient had prior TB treatment history with first line drugs only
9) Prior second-line TB treatment history: whether the patient had prior TB treatment history with second line TB drugs
10) Cavitary disease: whether the patient had cavitary disease based on chest x-ray results
12) Bilateral disease: whether the patient had bilateral disease based on chest x-ray results
13) Acid-Fast Bacilli (AFB) Smear results: whether the patient is AFB smear positive
14) Drug susceptibility testing (DST) results for all drugs tested

Statistical Analysis Plan: 

All individual patient datasets (including the requested clinical trial data through YODA project, and datasets from other studies) will be merged into one file and analyzed using SAS. Three types of drug-exposure will be considered in our meta-analysis: (i) specific drugs administered, (ii) duration of treatment regimen, and (iii) number of likely effective drugs used. Drugs are considered likely effective if susceptible on drug susceptibility testing, regardless of history of prior use. We will estimate odds of treatment success (defined as treatment cure or completion) compared to one of three alternate outcomes: (i) treatment failure or relapse; (ii) treatment failure, relapse or death; and (iii) treatment failure, relapse, death or default.
We will use random effects (random intercept and random slope) multi-variable logistic regression estimated via penalized quasi-likelihood (Proc Glimmix in SAS [12]) in order to estimate the adjusted odds and 95% CIs of treatment success associated with different treatment covariates [13-15]. As a sensitivity analysis, all models for primary analyses will also be estimated using adaptive quadrature (QUAD) [16]. Patients will be considered as clustered within studies and intercepts and slopes of the main exposure variables will be allowed to vary across studies; this is to account for otherwise unmeasured inter-study differences in patient populations, as well as center-specific differences in data ascertainment, measurement, and other factors. The variance of the study specific intercepts (here the baseline log odds of success in each cohort) and slopes (here treatment efficacy) will be interpreted to indicate how much these varied across the studies. We will report the average estimate of effect across studies from these models and the estimate inter-study variability and standard deviation of that variance, as well as the variance of the intercept and the standard deviation of that variance. Estimates of effect of each treatment parameter for each dataset will be adjusted for covariates: age, gender, HIV co-infection, AFB smear results, and past history of TB treatment, etc.
In addition to a traditional multivariable model, we will also use a propensity score–based method for adjusting for potential confounding [17].
Heterogeneity will also be explored visually using Forest plots of study specific estimates, and estimated quantitatively via the I2 and its associated 95% CI [15]. For these analyses, estimates of effect will be calculated separately for each study, adjusting for relevant patient-level covariates, and pooled using conventional meta-analytic techniques.

How did you learn about the YODA Project?: 
Associated Trials: 
<ol><li><a href="/node/520">NCT00449644 - CR011929, TMC207-TIDP13-C208, 2007-004462-40 - A Phase II, Placebo-controlled, Double-blind, Randomized Trial to Evaluate the Anti-bacterial Activity, Safety, and Tolerability of TMC207 in Subjects With Newly Diagnosed Sputum Smear-positive Pulmonary Infection With Multi-drug Resistant Mycobacterium Tuberculosis (MDR-TB).</a></li><li><a href="/node/521">NCT00910871 - CR012352, TMC207-TiDP13-C209, 2008-008444-25 - A Phase II, Open-label Trial With TMC207 as Part of a Multi-drug Resistant Tuberculosis (MDR-TB) Treatment Regimen in Subjects With Sputum Smear-positive Pulmonary Infection With MDR-TB</a></li></ol>
Make Publicly Available : 
Year of Data Access: 
2016
Associated Data: 
Reports

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