Bayesian hierarchical meta-analysis using individual participant data for modeling heterogeneous dropout patterns across multiple clinical trials

Missing data is a pervasive issue, notably in clinical trials and prospective studies employing a longitudinal design. This problem becomes particularly pronounced when dealing with data from multi-study clinical trials. Many established research teams undertake multiple clinical trials in closely related domains, each of which may exhibit distinct patterns of patient attrition/dropout. However, due to the shared group panel and study administration, these dropout patterns are often believed to exhibit similarities across these trials. While models addressing a single dropout mechanism have been extensively investigated, the analysis of heterogeneous dropout patterns remains understudied. To leverage heterogeneous data and integrate information from multiple missing mechanisms, we propose a new meta-analysis strategy based on individual participant data (IPD) to model observational-level dropout patterns over multiple trials and improve statistical inference via a Bayesian Hierarchical Model (BHM). We conducted extensive simulation studies to demonstrate the superiority of our method over existing methods in terms of reduced bias, smaller estimation variability, and higher statistical power. Finally, we applied our method to 13 clinical trials for schizophrenia research, exploring demographic and clinical determinants of dropout. We find that younger age and female gender are common predictors of dropout for all trials, while a higher Brief Psychiatric Rating Scale (BPRS) score is only a predictor in three individual trials.