Bayesian Meta-Analysis of Multiple Continuous Treatments with Individual Participant-Level Data: An Application to Antipsychotic Drugs - Spertus, Jacob (Med Decis Making, 2019)
Modeling dose-response relationships of drugs is essential to understanding their safety effects on patients 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 a Bayesian method to flexibly model dose-response relationships of binary outcomes with continuous treatment, permitting multiple evidence sources, treatment effect heterogeneity, and non-linear dose-response curves. 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. We define exposure as total cumulative dose (daily dose×duration) and convert to units equivalent to 100mg of olanzapine (OLZ doses). Averaging over the sample population of 5891 subjects, median dose ranged from 0 (placebo randomized participants) to 6.4 OLZ doses (paliperidone randomized participants). We found paliperidone to be least likely to cause excessive weight gain across a range of doses. Compared to 0 OLZ doses, at 5.0 OLZ doses, olanzapine subjects had a 15.6% (95% CrI: 6.7, 27.1) excess risk of weight gain; corresponding estimates for paliperidone and risperidone were 3.2% (1.5, 5.2) and 14.9% (0.0, 38.7) respectively. Moreover, compared to non-black participants, black participants were associated with a 6.8% (1.0, 12.4) greater risk of excessive weight gain at 10.0 OLZ doses of paliperidone. Nevertheless, our findings suggest that paliperidone is safer in terms of weight gain risk than risperidone or olanzapine for all participants at low to moderate cumulative OLZ doses.