Identifying Potential Adverse Events Dose-Response Relationships Via Bayesian Indirect and Mixed Treatment Comparison Models

Abstract

Patients and prescribers need to be fully informed regarding the safety profile of approved medications. This includes knowledge and information regarding whether an adverse event of interest exhibits a potential dose-response relationship. In order to thoroughly evaluate whether an adverse event rate increases with increasing dose level, evidence from multiple clinical trials needs to be combined and analyzed. The various clinical trials that need to be combined often include different dose levels. If one evaluates the dose-response relationship by including only the trials with all of the common dose levels, this will lead to the exclusion of potentially several clinical trials as well as dose levels, and thus the loss of important information. Other methods, such as crudely pooling patients on the same dose level across different studies, are subject to bias due to the breakdown of randomization. It is preferable to include all studies and relevant dose levels, even if all studies do not contain the same dose levels. Bayesian methodology has been shown to be useful in the context of indirect and mixed treatment comparison methods, to combine information from different therapies in different studies in order to make treatment effect inferences. This type of approach is foundational to the models presented here, but instead of modeling different dose arms in different studies, we extend the methodology to allow for assessment of the dose-response relationship across multiple clinical trials. In this paper, we propose three Bayesian indirect/mixed treatment comparison models to assess adverse event dose-response relationships. These three models are designed to handle binary responses and time to event responses. We apply the methods to real data sets and demonstrate that our proposed methods are useful in discovering potential dose-response relationships.