Adjusting for intercurrent events using Bayesian joint models for longitudinal outcomes in clinical trials
Our Bayesian joint modeling approach effectively addresses terminal intercurrent events in both the design and analysis phases of clinical trials. By explicitly accounting for event-related truncation of longitudinal follow-up, it improves the precision and reliability of treatment effect estimation when outcome measurement is incomplete.
Authors
- José Dianti (ORCID: https://orcid.org/0000-0003-2016-7003)
- Ewan C. Goligher (ORCID: https://orcid.org/0000-0002-0990-6701)
- Yongdong Ouyang (ORCID: https://orcid.org/0000-0002-8692-2991)
- Wen Teng (ORCID: https://orcid.org/0000-0002-0132-503X)
- Anna Heath
- Niall D. Ferguson
Institutions
- Roswell Park Comprehensive Cancer Center (US)
- University Health Network (CA)
- University of Toronto (CA)
- Lunenfeld-Tanenbaum Research Institute (CA)
- Hospital for Sick Children (CA)
- Public Health Ontario (CA)
- Centro de Educación Médica e Investigaciones Clínicas Norberto Quirno (AR)
- SickKids Foundation (CA)
- Institute for Clinical Evaluative Sciences (CA)
- University College London (GB)
Publication Details
- Journal
- Contemporary Clinical Trials Communications
- Published
- 2026-02-14
- DOI
- https://doi.org/10.1016/j.conctc.2026.101618
- Primary Topic
- Statistical Methods in Clinical Trials
- Type
- article
- Field-Weighted Citation Impact
- 0.00