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  string(91) "Individual Participant-Level Data, which includes Full CSR and all supporting documentation"
  ["property_scientific_abstract"]=>
  string(2435) "Background: Many RCTs evaluate the average effects of antipsychotic drugs on patients with schizophrenia or bipolar disorder. Observed from clinical practice, the effects may vary across patients? profile (effect modifiers). Such effect modifiers can be discrete or continuous. When they are discrete, subgroup effects are of interest; when they are continuous, we may be interested in how heterogeneous effects vary as a (smooth) function of the modifiers, which is more challenging to be estimated. One major challenge in the estimation of effect heterogeneity is that the sample size of a single randomized trial is typically not enough to precisely estimate effects. Therefore, there is interest in synthesizing evidence across the different trials to improve precision of estimators of heterogeneous treatment efficacy. Furthermore, when combining information across trials, the trial samples typically do not represent a common target population of substantive interest. This raises the question of how to combine information from multiple trials in a way that is interpretable in the context of some meaningful target population of interest while using evidence across trials to improve efficiency. 
Objective: We are going to develop and evaluate the performance of new statistical methods for transporting heterogeneous effects from multiple trials to an ?external? target population of non-randomized individuals or an ?internal? target population underlying one of the trials contributing information to the analysis. Part of the methodological evaluation will involve applying the methods developed to the datasets requested here.
Study Design: We will conduct a causally interpretable meta-analysis to estimate and transport heterogeneous effects using the datasets requested.
Participants: The analysis will be restricted to all participants that are 18 years or older with a DSM-IV diagnosis of schizophrenia or Bipolar I disorder.
Main Outcome Measure(s): For participants with schizophrenia, we will focus on Positive and Negative Syndrome Scale total score; for participants with bipolar disorder, we will focus on the Young Mania Rating Scale.
Statistical Analysis: The methods developed will be doubly robust and non-parametrically efficient. Furthermore, the methods allow us to use machine learning to flexibly model how the effects vary according to the patients? characteristics." ["project_brief_bg"]=> string(2376) "Clinical trial results are commonly used to justify treatment options in different populations than the trial was conducted in (e.g., all participants who are eligible for a clinical trial rather than those that agreed to participate, a different geographic region, or a different clinical setting). Such justification requires transporting clinical trial results to the target population for which the treatment is intended. Depending on the clinical interests, the target population can be a combination of trials, an external population, or a specific internal trial. A significant barrier to the practical utility of methods for transporting treatment effects is the lack of methods for handling heterogeneous treatment effect estimation. Methods for generalizing the average treatment effects have been extensively studied [1] However, oftentimes, treatments effects may vary over patient characteristics (e.g., sex, race, disease severity), which are known as effect modifiers. When that is the case, the average treatment effect (marginal over covariates) is not sufficiently informative to support clinical or policy decisions. [2] Such effect modifiers can be discrete; in which case the subgroup treatment effects can provide more insight for decision making. More statistically challenging but also of substantial clinical relevance, when the modifiers are continuous, one may be interested in how the treatment effect changes as a (smooth) function of a continuous variable. The estimation and inference of such parameter of interest usually require a large sample size, which a single trial can rarely achieve, but is possible when combining data from multiple sources. Therefore, we propose to develop and validate methods for transporting heterogeneous treatment effects from multi-source data to an external or an internal target population, substantially improving the clinical utility of previously developed methods. The methods developed will be evaluated using the schizophrenia and bipolar disorder datasets that we request, using approaches that account for the differences in the populations underlying each trial. This will lead to results that are more generalizable to clinical practice and will provide understanding of the amount of treatment effect heterogeneity among different populations and how representative they are of real-world settings." ["project_specific_aims"]=> string(479) "AIM 1: Develop methods for transporting subgroup treatment effects from multiple trials to an external or an internal target population.
AIM 2: Develop methods for transporting treatment effects conditional on a continuous effect modifier from multiple trials to an external or an internal target population.
AIM 3: Apply and empirically evaluate the methods developed in Aim 1 using the schizophrenia and bipolar disorder trials requested as a part of this proposal." ["project_study_design"]=> array(2) { ["value"]=> string(8) "meth_res" ["label"]=> string(23) "Methodological research" } ["project_study_design_exp"]=> string(0) "" ["project_purposes"]=> array(1) { [0]=> array(2) { ["value"]=> string(37) "Develop or refine statistical methods" ["label"]=> string(37) "Develop or refine statistical methods" } } ["project_purposes_exp"]=> string(0) "" ["project_software_used"]=> array(2) { ["value"]=> string(1) "R" ["label"]=> string(1) "R" } ["project_software_used_exp"]=> string(0) "" ["project_research_methods"]=> string(923) "In the analysis we will include all randomized trials that evaluate the effect of antipsychotic treatments on schizophrenia and bipolar disorder. To implement the analyses, we need individual level data on all participants. This includes outcome information, treatment information, and individual level characteristics that allow us to account for differences in the underlying populations (e.g., demographic information, family history, severity of mental illness at baseline etc.). The analysis will include participants who are older than 18 and are diagnosed with schizophrenia or bipolar I disorder using the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) diagnosis of schizophrenia or bipolar I disorder. We are requesting individual level data from all trials that involve paliperidone, paliperidone palmitate and/or risperidone (identified through the trial search on the YODA website)." ["project_main_outcome_measure"]=> string(345) "For participants with schizophrenia, we will focus on Positive and Negative Syndrome Scale total score; for participants with bipolar disorder we will focus on the Young Mania Rating Scale. For the analysis we will focus on the difference in these measures from baseline to end of study and to differences between groups at the end of follow-up." ["project_main_predictor_indep"]=> string(479) "We are requesting information on all covariates collected in the studies. This includes demographic variables (age, race, ethnicity, etc.), clinical information (BMI, drug abuse, alcohol abuse etc.), and severity of symptoms at baseline. These variables will be used as potential effect modifiers and to account for differences in the covariate distributions between randomized trials, allowing us to transport inferences about heterogeneous treatment effects between populations" ["project_other_variables_interest"]=> string(80) "We will use categories and variable coding as defined in the individual studies." ["project_stat_analysis_plan"]=> string(806) "For Aim 1 and 2, we will use semi/none-parametric efficiency theory developed in the context of causal inference to develop methods for generalizing/transporting heterogeneous treatment effects from multiple clinical trials to an external population or a specific internal trial. This involves extending our group?s prior work to handling treatment effects transportability using debiased machine learning methods and projection-based flexible data-adaptive estimation [3-5]. This also involves identifying conditions under which such transportability analysis can be done and using falsification test of these assumptions to decide which datasets to include in the analysis.
For Aim 3 we will apply the methods developed in Aims 1 and 2 of this proposal to the trials requested in this application." ["project_timeline"]=> string(343) "These analyses are a part of a PCORI funded proposal with a funding period from 7/1/2022-6/30/2025. In year 1 we expect to develop the methods described in Aim 1 and 2 of the proposal and clean and harmonize the datasets. In years 2 and 3, for Aim 3 of the project, we expect to analyze the data using the methods developed under Aims 1 and 2." ["project_dissemination_plan"]=> string(364) "The results from this project will be journal publications and presentations at research meetings. The target audience will be statisticians, clinical trialists, and psychiatrists and the publication and presentation venues will focus on these groups (e.g., submit to more methodological work in biostatistics journal and more applied work in psychiatry journals)." ["project_bibliography"]=> string(1130) "

[1] Dahabreh, I. J., Robertson, S. E., Petito, L. C., Hernn, M. A., & Steingrimsson, J. A. (2019). Efficient and robust methods for causally interpretable meta?analysis: Transporting inferences from multiple randomized trials to a target population. Biometrics.
[2] Robertson, S. E., Steingrimsson, J. A., & Dahabreh, I. J. (2021). Regression-based estimation of heterogeneous treatment effects when extending inferences from a randomized trial to a target population. arXiv preprint arXiv:2110.00107.
[3] Semenova, V., & Chernozhukov, V. (2021). Debiased machine learning of conditional average treatment effects and other causal functions. The Econometrics Journal, 24(2), 264-289.
[4] Kennedy, E. H., Balakrishnan, S., & Wasserman, L. (2021). Semiparametric counterfactual density estimation. arXiv preprint arXiv:2102.12034.
[5] Kennedy, E. H., Lorch, S., & Small, D. S. (2019). Robust causal inference with continuous instruments using the local instrumental variable curve. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 81(1), 121-143.

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2022-5062

General Information

How did you learn about the YODA Project?: Colleague

Conflict of Interest

Request Clinical Trials

Associated Trial(s):
  1. NCT00645099 - A Prospective Randomized Open-label 6-Month Head-To-Head Trial to Compare Metabolic Effects of Paliperidone ER and Olanzapine in Subjects With Schizophrenia
  2. NCT00334126 - 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
  3. NCT00086320 - 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
  4. NCT00589914 - 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
  5. NCT00604279 - 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
  6. NCT00590577 - 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
  7. NCT00111189 - 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
  8. NCT00210717 - 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
  9. NCT00119756 - 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
  10. NCT00210548 - 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
  11. NCT00101634 - 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
  12. NCT00391222 - 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
  13. NCT00132678 - 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
  14. NCT00094926 - 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
  15. NCT00237289 - Topiramate Versus Placebo as add-on Treatment in Patients With Bipolar Disorder in the Outpatient Setting
  16. NCT00240721 - A Randomized, Double-Blind, Multicenter, Placebo-Controlled 12-Week Study Of The Safety And Efficacy Of Two Doses Of Topiramate For The Treatment Of Acute Manic Or Mixed Episodes In Subjects With Bipolar I Disorder With An Optional Open-Label Extension
  17. NCT00037674 - A Randomized, Double-Blind, Multicenter, Placebo-Controlled 12-Week Study of the Safety and Efficacy of Two Doses of Topiramate for the Treatment of Acute Manic or Mixed Episodes in Patients With Bipolar I Disorder With an Optional Open-Label Extension
  18. NCT00035230 - A Randomized, Double-Blind, Multicenter, Placebo-Controlled 12-Week Study of the Safety and Efficacy of Topiramate in Patients With Acute Manic or Mixed Episodes of Bipolar I Disorder With an Optional Open-Label Extension
  19. NCT00249132 - A Canadian multicenter placebo-controlled study of fixed doses of risperidone and haloperidol in the treatment of chronic schizophrenic patients
  20. NCT00253162 - 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
  21. NCT00378092 - A Prospective Study of the Clinical Outcome Following Treatment Discontinuation After Remission in First-Episode Schizophrenia
  22. NCT00299715 - 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
  23. NCT00309699 - 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
  24. NCT00309686 - 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
  25. NCT00752427 - 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
  26. NCT00077714 - 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
  27. NCT00083668 - 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
  28. NCT00074477 - 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
  29. NCT00078039 - Trial Evaluating Three Fixed Dosages of Paliperidone Extended-Release (ER) Tablets and Olanzapine in the Treatment of Patients With Schizophrenia
What type of data are you looking for?:

Request Clinical Trials

Data Request Status

Status: Ongoing

Research Proposal

Project Title: Heterogeneous treatment effects estimation with multi-source data: evidence from bipolar disorder and schizophrenia trials

Scientific Abstract: Background: Many RCTs evaluate the average effects of antipsychotic drugs on patients with schizophrenia or bipolar disorder. Observed from clinical practice, the effects may vary across patients? profile (effect modifiers). Such effect modifiers can be discrete or continuous. When they are discrete, subgroup effects are of interest; when they are continuous, we may be interested in how heterogeneous effects vary as a (smooth) function of the modifiers, which is more challenging to be estimated. One major challenge in the estimation of effect heterogeneity is that the sample size of a single randomized trial is typically not enough to precisely estimate effects. Therefore, there is interest in synthesizing evidence across the different trials to improve precision of estimators of heterogeneous treatment efficacy. Furthermore, when combining information across trials, the trial samples typically do not represent a common target population of substantive interest. This raises the question of how to combine information from multiple trials in a way that is interpretable in the context of some meaningful target population of interest while using evidence across trials to improve efficiency.
Objective: We are going to develop and evaluate the performance of new statistical methods for transporting heterogeneous effects from multiple trials to an ?external? target population of non-randomized individuals or an ?internal? target population underlying one of the trials contributing information to the analysis. Part of the methodological evaluation will involve applying the methods developed to the datasets requested here.
Study Design: We will conduct a causally interpretable meta-analysis to estimate and transport heterogeneous effects using the datasets requested.
Participants: The analysis will be restricted to all participants that are 18 years or older with a DSM-IV diagnosis of schizophrenia or Bipolar I disorder.
Main Outcome Measure(s): For participants with schizophrenia, we will focus on Positive and Negative Syndrome Scale total score; for participants with bipolar disorder, we will focus on the Young Mania Rating Scale.
Statistical Analysis: The methods developed will be doubly robust and non-parametrically efficient. Furthermore, the methods allow us to use machine learning to flexibly model how the effects vary according to the patients? characteristics.

Brief Project Background and Statement of Project Significance: Clinical trial results are commonly used to justify treatment options in different populations than the trial was conducted in (e.g., all participants who are eligible for a clinical trial rather than those that agreed to participate, a different geographic region, or a different clinical setting). Such justification requires transporting clinical trial results to the target population for which the treatment is intended. Depending on the clinical interests, the target population can be a combination of trials, an external population, or a specific internal trial. A significant barrier to the practical utility of methods for transporting treatment effects is the lack of methods for handling heterogeneous treatment effect estimation. Methods for generalizing the average treatment effects have been extensively studied [1] However, oftentimes, treatments effects may vary over patient characteristics (e.g., sex, race, disease severity), which are known as effect modifiers. When that is the case, the average treatment effect (marginal over covariates) is not sufficiently informative to support clinical or policy decisions. [2] Such effect modifiers can be discrete; in which case the subgroup treatment effects can provide more insight for decision making. More statistically challenging but also of substantial clinical relevance, when the modifiers are continuous, one may be interested in how the treatment effect changes as a (smooth) function of a continuous variable. The estimation and inference of such parameter of interest usually require a large sample size, which a single trial can rarely achieve, but is possible when combining data from multiple sources. Therefore, we propose to develop and validate methods for transporting heterogeneous treatment effects from multi-source data to an external or an internal target population, substantially improving the clinical utility of previously developed methods. The methods developed will be evaluated using the schizophrenia and bipolar disorder datasets that we request, using approaches that account for the differences in the populations underlying each trial. This will lead to results that are more generalizable to clinical practice and will provide understanding of the amount of treatment effect heterogeneity among different populations and how representative they are of real-world settings.

Specific Aims of the Project: AIM 1: Develop methods for transporting subgroup treatment effects from multiple trials to an external or an internal target population.
AIM 2: Develop methods for transporting treatment effects conditional on a continuous effect modifier from multiple trials to an external or an internal target population.
AIM 3: Apply and empirically evaluate the methods developed in Aim 1 using the schizophrenia and bipolar disorder trials requested as a part of this proposal.

Study Design: Methodological research

What is the purpose of the analysis being proposed? Please select all that apply.: Develop or refine statistical methods

Software Used: R

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: In the analysis we will include all randomized trials that evaluate the effect of antipsychotic treatments on schizophrenia and bipolar disorder. To implement the analyses, we need individual level data on all participants. This includes outcome information, treatment information, and individual level characteristics that allow us to account for differences in the underlying populations (e.g., demographic information, family history, severity of mental illness at baseline etc.). The analysis will include participants who are older than 18 and are diagnosed with schizophrenia or bipolar I disorder using the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) diagnosis of schizophrenia or bipolar I disorder. We are requesting individual level data from all trials that involve paliperidone, paliperidone palmitate and/or risperidone (identified through the trial search on the YODA website).

Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study: For participants with schizophrenia, we will focus on Positive and Negative Syndrome Scale total score; for participants with bipolar disorder we will focus on the Young Mania Rating Scale. For the analysis we will focus on the difference in these measures from baseline to end of study and to differences between groups at the end of follow-up.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: We are requesting information on all covariates collected in the studies. This includes demographic variables (age, race, ethnicity, etc.), clinical information (BMI, drug abuse, alcohol abuse etc.), and severity of symptoms at baseline. These variables will be used as potential effect modifiers and to account for differences in the covariate distributions between randomized trials, allowing us to transport inferences about heterogeneous treatment effects between populations

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: We will use categories and variable coding as defined in the individual studies.

Statistical Analysis Plan: For Aim 1 and 2, we will use semi/none-parametric efficiency theory developed in the context of causal inference to develop methods for generalizing/transporting heterogeneous treatment effects from multiple clinical trials to an external population or a specific internal trial. This involves extending our group?s prior work to handling treatment effects transportability using debiased machine learning methods and projection-based flexible data-adaptive estimation [3-5]. This also involves identifying conditions under which such transportability analysis can be done and using falsification test of these assumptions to decide which datasets to include in the analysis.
For Aim 3 we will apply the methods developed in Aims 1 and 2 of this proposal to the trials requested in this application.

Narrative Summary: Clinical trials are almost always conducted with a specific target population in mind, but data collected is rarely a random sample of that population. This project aims to develop new statistical methods that allow us to transport/generalize heterogeneous treatment effects from multiple clinical trials to a target population of interest, external to the trials (e.g., sampled separately) or internal to the trials (e.g., defined as the population underlying one of the trials). The methods will be evaluated using data from several bipolar disorder and schizophrenia randomized trials creating generalizable evidence about treatment efficacy.

Project Timeline: These analyses are a part of a PCORI funded proposal with a funding period from 7/1/2022-6/30/2025. In year 1 we expect to develop the methods described in Aim 1 and 2 of the proposal and clean and harmonize the datasets. In years 2 and 3, for Aim 3 of the project, we expect to analyze the data using the methods developed under Aims 1 and 2.

Dissemination Plan: The results from this project will be journal publications and presentations at research meetings. The target audience will be statisticians, clinical trialists, and psychiatrists and the publication and presentation venues will focus on these groups (e.g., submit to more methodological work in biostatistics journal and more applied work in psychiatry journals).

Bibliography:

[1] Dahabreh, I. J., Robertson, S. E., Petito, L. C., Hernn, M. A., & Steingrimsson, J. A. (2019). Efficient and robust methods for causally interpretable meta?analysis: Transporting inferences from multiple randomized trials to a target population. Biometrics.
[2] Robertson, S. E., Steingrimsson, J. A., & Dahabreh, I. J. (2021). Regression-based estimation of heterogeneous treatment effects when extending inferences from a randomized trial to a target population. arXiv preprint arXiv:2110.00107.
[3] Semenova, V., & Chernozhukov, V. (2021). Debiased machine learning of conditional average treatment effects and other causal functions. The Econometrics Journal, 24(2), 264-289.
[4] Kennedy, E. H., Balakrishnan, S., & Wasserman, L. (2021). Semiparametric counterfactual density estimation. arXiv preprint arXiv:2102.12034.
[5] Kennedy, E. H., Lorch, S., & Small, D. S. (2019). Robust causal inference with continuous instruments using the local instrumental variable curve. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 81(1), 121-143.