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arXiv · 1802.05186

Bayesian Meta-Analysis of Multiple Continuous Treatments: An Application to Antipsychotic Drugs

Jacob Spertus·Marcela Horvitz-Lennon·Sharon-Lise Normand
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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 trials. Averaging over the sample population, we found that olanzapine contributed to a 15.6% (95% CrI: 6.7, 27.1) excess risk of weight gain at a 500mg cumulative dose. Paliperidone conferred a 3.2% (95% CrI: 1.5, 5.2) and risperidone a 14.9% (95% CrI: 0.0, 38.7) excess risk at 500mg olanzapine equivalent cumulative doses. Blacks had an additional 6.8% (95% CrI: 1.0, 12.4) risk of weight gain over non-blacks at 1000mg olanzapine equivalent cumulative doses of paliperidone.

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Jacob Spertus, Marcela Horvitz-Lennon, Sharon-Lise Normand. 2018-02-14. Bayesian Meta-Analysis of Multiple Continuous Treatments: An Application to Antipsychotic Drugs. https://arxiv.org/abs/1802.05186

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