Abstract
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.
Original language | English (US) |
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Pages (from-to) | 34-49 |
Number of pages | 16 |
Journal | Biostatistics |
Volume | 23 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2022 |
Keywords
- Bayesian nonparametrics
- Brain cancer trial
- Causal inference
- Identification assumptions
- Principal stratification
- Sensitivity analysis
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty