Bayesian Meta-Analysis of Multiple Continuous Treatments: An Application to Antipsychotic Drugs
Jacob Spertus, Marcela Horvitz-Lennon, and Sharon-Lise Normand

TL;DR
This paper introduces Bayesian meta-analysis methods for modeling dose-response relationships of continuous treatments with binary outcomes, applied to antipsychotic drugs and weight gain risk, accounting for heterogeneity across trials.
Contribution
It develops a flexible Bayesian hierarchical framework for meta-analyzing dose-response effects of multiple treatments on binary outcomes, incorporating heterogeneity and non-linear responses.
Findings
Olanzapine increases weight gain risk by 15.6% at 500mg.
Paliperidone increases weight gain risk by 3.2% at 500mg.
Blacks have an additional 6.8% risk at 1000mg olanzapine equivalent doses.
Abstract
Modeling dose-response relationships of drugs is essential to understanding their effect on patient outcomes under realistic circumstances. While intention-to-treat analyses of clinical trials provide the effect of assignment to a particular drug and dose, they do not capture observed exposure after factoring in non-adherence and dropout. We develop Bayesian methods to flexibly model dose-response relationships of binary outcomes with continuous treatment, allowing for treatment effect heterogeneity and a non-linear response surface. We use a hierarchical framework for meta-analysis with the explicit goal of combining information from multiple trials while accounting for heterogeneity. In an application, we examine the risk of excessive weight gain for patients with schizophrenia treated with the second generation antipsychotics paliperidone, risperidone, or olanzapine in 14 clinical…
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Taxonomy
TopicsMeta-analysis and systematic reviews · Statistical Methods in Clinical Trials · Health Systems, Economic Evaluations, Quality of Life
