A Bayesian nonparametric approach for evaluating the causal effect of treatment in randomized trials with semi-competing risks
Year: 2020
Type: article
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.
Source: Biostatistics
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: BAYESIAN APPROACHES FOR MISSINGNESS AND CAUSALITY IN CANCER AND BEHAVIOR STUDIES, Non-Parametric Bayesian Methods for Causal Inference, Collaborative Research: New Bayesian Methods for Modeling the Effect of Antiretroviral Drugs on Depressive Symptomatology in HIV Patients, The Johns Hopkins Center for AIDS Research (JHU CFAR)
OpenAlex ID: W3014401924