Lead Investigator: Hwanhee Hong, Duke University
Title of Proposal Research: Combining data sources to identify effect moderation for personalized mental health treatment
Vivli Data Request: 7599
Funding Source: This work has been funded by NIMH (R01MH126856) and PCORI (ME-2020C3-21145)
Potential Conflicts of Interest: None
Summary of the Proposed Research:
Efficiently identifying the right treatment for the right patient can improve quality of healthcare for individuals and populations. Treatments for depression and schizophrenia are well documented to exhibit variable effectiveness because “effect moderators,” defined as known and unknown individual, disease-specific, genetic, environmental, and other characteristics that impact the effectiveness of treatments or exposures. Finding ways to identify and leverage effect moderators at the point of care to facilitate clinical decision-making would improve efficiency, quality, and outcomes of healthcare, including behavioral healthcare. Without cut-and-dried tests to provide definitive diagnoses, and in the setting of illnesses with heterogeneous presentations and response to treatments, the field of mental health faces unique challenges in the quest to determine “what works for whom” – and why. Identifying such effect moderators is crucial for personalized delivery of treatment and prevention interventions, but doing so is incredibly difficult using standard study designs. This work will synthesize, extend, and apply methods for identifying effect moderators when multiple studies are available, with a particular focus on the complexities in mental health research. The methods will apply broadly and will be illustrated in an example estimating the effects of medication treatment for schizophrenia and major depressive disorder, using data from 7 randomized controlled trials and non-experimental data from the Duke University and Johns Hopkins Health System electronic health record. The work will: 1) Extend moderation methods for scenarios with multiple randomized experiments and 2) Develop methods for using data from combined datasets with both experimental and non-experimental designs to identify effect moderation. By developing methods to take full advantage of both experimental and non-experimental data this work has the potential to move towards personalized mental health, thus improving how we prevent and treat mental health challenges in the population.
Statistical Analysis Plan:
Our proposed project spans a broad spectrum that includes on one end the situation where putative effect moderators have been specified and on the other end the situation where effect moderation is assumed to be via some unknown function of a set of covariates. We use Bayesian parametric models to handle the former case and nonparametric machine learning methods to handle the latter case. In addition, to deal with the problem that randomized clinical trials (RCTs) are generally not powered to test moderation effects, we propose to combine data from multiple studies: multiple RCTs, and RCTs plus non-experimental electronic health records (EHR) data. Our work builds on Bayesian hierarchical modeling with adaptive borrowing across multiple data and extends existing machine learning methods for effect modeling (developed for the single study setting) to handle multiple studies.
For the RCT data source, we will use two examples: 1) the comparative treatment effect of paliperidone palmitate vs. risperidone long-acting injection (LAI) for individuals with schizophrenia, and 2) the comparative treatment effect of duloxetine vs. vortioxetine for individuals with major depressive disorder. As of February 2022, we have identified 7 RCTs (3 for schizophrenia and 4 for major depressive disorder) available in the Vivli trials resource. The 3 schizophrenia RCTs had common inclusion criteria such as age greater than or equal to 18, schizophrenia diagnosed by Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV (295.10, 295.20, 295.30, 295.60, 295.90), baseline Positive and Negative Syndrome Scale (PANSS) total score is between 60 and 120, and baseline Body Mass Index (BMI) greater than or equal to 15 kg/m^2. The 4 major depressive disorder RCTs had common inclusion criteria such as age greater than or equal to 18, major depressive episode according to DSM-IV-TR (296.3x), baseline Montgomery-Åsberg Depression Rating Scale (MADRS) total score is greater than or equal to 26, and baseline Clinical Global Impression-Severity of Illness (CGI-S) greater than or equal to 4. Our methods will develop methods to combine multiple data all together in a principled way rather than analyzing individual studies. We will measure between-study variability by adding random effects. Missing data will be assessed thoroughly before conducting analyses (e.g., report missingness proportion for all variables) and imputed using proper methods such as multiple imputation.
Requested Studies:
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
Data Contributor: Johnson & Johnson
Study ID: NCT00604279
Sponsor ID: CR013150
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
Data Contributor: Johnson & Johnson
Study ID: NCT00589914
Sponsor ID: CR012289
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
Data Contributor: Johnson & Johnson
Study ID: NCT00210717
Sponsor ID: CR004195
A Phase 3, Randomized, Double-Blind, Parallel-Group, Placebo-Controlled, Duloxetine-Referenced, Fixed-Dose Study Comparing the Efficacy and Safety of 2 Doses (15 and 20 mg) of Lu AA21004 in Acute Treatment of Adults With Major Depressive Disorder
Data Contributor: Takeda
Study ID: NCT01153009
Sponsor ID: LuAA21004_315
A Randomised, Double-blind, Parallel-group, Placebo-controlled, Duloxetine-referenced, Fixed-dose Study Evaluating the Efficacy and Safety of Lu AA21004 (15 and 20 mg/Day) in the Acute Treatment of Adult Patients With Major Depressive Disorder
Data Contributor: Lundbeck
Study ID: NCT01140906
Sponsor ID: 13267A
A Randomized, Double-Blind, Parallel-Group, Placebo-Controlled, Active-Referenced, Fixed-Dose Study Comparing the Efficacy and Safety of 2 Doses of Lu AA21004 in Acute Treatment of Adults With Major Depressive Disorder
Data Contributor: Takeda
Study ID: NCT00672620
Sponsor ID: LuAA21004_304
A Randomised, Double-blind, Parallel-group, Placebo-controlled, Duloxetine-referenced, Fixed-dose Study Evaluating the Efficacy and Safety of Three Dosages of [Vortioxetine] Lu AA21004, in Acute Treatment of Major Depressive Disorder
Data Contributor: Lundbeck
Study ID: NCT00635219
Sponsor ID: 11984A
Public Disclosures:
Lupton Brantner, C., Quynh Nguyen, T., Tang, T., Zhao, C., Hong, H. and Stuart, E.A., 2023. Comparing Machine Learning Methods for Estimating Heterogeneous Treatment Effects by Combining Data from Multiple Randomized Controlled Trials. arXiv e-prints, pp.arXiv-2303. Doi : 10.48550/arXiv.2303.16299