A bayesian nonparametric approach for evaluating the causal effect of treatment in randomized trials with semi-competing risks

Yanxun Xu, Daniel Scharfstein, Peter Müller, Michael Daniels

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Mar 20 2019

Keywords

  • Bayesian nonparametrics
  • Brain cancer trial
  • Causal inference
  • Identification assumptions
  • Principal stratification
  • Sensitivity analysis

ASJC Scopus subject areas

  • General

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