A Bayesian nonparametric approach for evaluating the causal effect of treatment in randomized trials with semi-competing risks. (4th April 2020)

• Record Type: Journal Article
• Authors: Xu, Yanxun; Scharfstein, Daniel; Müller, Peter; Daniels, Michael
• Is Part Of: Biostatistics. Volume 23:Number 1(2022)
• Page Start: 34
• Page End: 49
• Publication Date: 2020-04-04
• Subjects: Bayesian nonparametrics -- Brain cancer trial -- Causal inference -- Identification assumptions -- Principal stratification -- Sensitivity analysis; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxaa008
• Languages: English
• ISSNs: 1465-4644

Abstract

Summary: We develop a Bayesian nonparametric (BNP) approach to evaluate the causal effect of treatment in a randomized trial where a nonterminal event may be censored by a terminal event, but not vice versa (i.e., semi-competing risks). Based on the idea of principal stratification, we define a novel estimand for the causal effect of treatment on the nonterminal event. We introduce identification assumptions, indexed by a sensitivity parameter, and show how to draw inference using our BNP approach. We conduct simulation studies and illustrate our methodology using data from a brain cancer trial. The R code implementing our model and algorithm is available for download at https://github.com/YanxunXu/BaySemiCompeting .