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

Eshetu Tefera, Chixiang Chen, Robert Buchanan, Deanna Kelly and Shuo Chen

Journal of Applied Statistics, 2026, vol. 53, issue 9, 1648-1665

Abstract: 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.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2025.2574650 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:53:y:2026:i:9:p:1648-1665

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2025.2574650

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().