Building a dose toxo-equivalence model from a Bayesian meta-analysis of published clinical trials

Abstract

In clinical practice medications are often interchanged in treatment protocols when a patient negatively reacts to their first line of therapy. Although switching between medications is common, clinicians often lack structured guidance when choosing the initial dose and frequency of a new medication, given the former with respect to risk of adverse events. In this paper we propose to establish this dose toxo-equivalence relationship using published clinical trial results with one or both drugs of interest via a Bayesian meta-analysis model that accounts for both within- and between-study variances. With the posterior parameter samples from this model, we compute median and $95\mathrm{\%}$ credible intervals for equivalent dose pairs of the two drugs that are predicted to produce equal rates of an adverse outcome, relying solely on study-level information. Via extensive simulations, we show that this approach approximates well the true dose toxo-equivalence relationship, considering different study designs, levels of between-study variance, and the inclusion/exclusion of nonconfounder/nonmodifier subject-level covariates in addition to study-level covariates. We compare the performance of this study-level meta-analysis estimate to the equivalent individual patient data meta-analysis model and find comparable bias and minimal efficiency loss in the study-level coefficients used in the dose toxo-equivalence relationship. Finally, we present the findings of our dose toxo-equivalence model applied to two chemotherapy drugs, based on data from 169 published clinical trials.