Literature-based meta-analysis of adverse events accounting for heterogeneous follow-up duration in oncology clinical trials

It is difficult to understand the safety profile of drugs based on a single clinical trial since clinical trials are often designed to prove efficacies, and sample size is not powered for safety assessment. Thus, meta-analysis would be a valuable tool to infer the safety profiles utilizing multiple studies. Individual clinical trials usually report the incidence proportions of adverse events (AEs) observed in the study. The follow-up duration may be study-specific, and furthermore different between the treatment groups within a single study. It often occurs in oncology clinical trials and if this is the case, it is hard to interpret the aggregated relative risk of AEs and compare the risk of AEs between the treatment groups with the standard meta-analysis techniques. The progression-free survival or the overall survival is often used as the primary endpoint in oncology clinical trials and the Kaplan-Meier estimates of the survival functions for the primary endpoint are often demonstrated graphically, which give us information of the follow-up duration of the AEs. We propose novel meta-analysis methods for AEs that address differences in follow-up durations by efficiently utilizing the Kaplan-Meier estimates of the primary endpoint. We adapt our approach using both simulated data and real data from a meta-analysis of bevacizumab. Simulation studies demonstrate that the proposed methods perform well when follow-up time differs between trials and groups.