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A Bayesian approach for estimating the survivor average causal effect when outcomes are truncated by death in cluster-randomized trials

Tong, G., Li, F., Chen, X. , Hirani, S. P. ORCID: 0000-0002-1577-8806, Newman, S. P. ORCID: 0000-0001-6712-6079, Wang, W. & Harhay, M. O. (2023). A Bayesian approach for estimating the survivor average causal effect when outcomes are truncated by death in cluster-randomized trials. American Journal of Epidemiology, 192(6), pp. 1006-1015. doi: 10.1093/aje/kwad038


Many studies encounter clustering due to multicenter enrollment and non-mortality outcomes, such as quality-of-life, that are truncated due to death; i.e., missing not at random and nonignorable. Traditional missing data methods and target causal estimands are suboptimal for statistical inference in the presence of these combined issues, which are especially common in multicenter studies and cluster-randomized trials (CRTs) among the elderly or seriously ill. Using principal stratification, we developed a Bayesian estimator that jointly identifies the always-survivor principal stratum in a clustered/hierarchical data setting and estimates the average treatment effect among them (i.e., the survivor average causal effect, SACE). In simulations, we observed low bias and good coverage with our method. In a motivating CRT, the SACE and the estimate from complete case analysis differed in magnitude, but both were small, and neither was incompatible with a null effect. However, the SACE estimate has a clear causal interpretation. The option to assess the rigorously defined SACE estimand in studies with informative truncation and clustering can provide additional insight into an important subset of study participants. Based on the simulation study and CRT reanalysis, we provide practical recommendations for using the SACE in CRTs and code to support future research.

Publication Type: Article
Additional Information: © The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords: Cluster-randomized trials, survivor average causal effect, counterfactual outcomes, always survivors, death truncation, quality of life, Bayesian estimation
Subjects: Q Science > QA Mathematics
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Departments: School of Health & Psychological Sciences > Healthcare Services Research & Management
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