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Activity Number: 7 - Bayesian Nonparametrics in Causal Inference
Type: Invited
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #325454 Presentation
Title: A Bayesian Nonparametric Approach for Causal Inference with Semi-Competing Risks
Author(s): Michael Daniels* and Peter Müller and Yanxun Xu and Daniel Scharfstein
Companies: University of Florida and University of Texas Austin and Johns Hopkins University and Johns Hopkins University
Keywords: Causal inference; Bayesian nonparametrics

We develop a Bayesian nonparametric (BNP) model to assess the treatment effect in semi-competing risks, where a nonterminal event may be censored by a terminal event, but not vice versa. Semi-competing risks are common in brain cancer trials with death being censored by cerebellar progression. We propose a flexible BNP approach to model the joint distribution of progression and death events, thereby effectively inferring the marginal distributions of progression time and death time, characterizing within-subject dependence structure, predicting the progression and death times given a patient's covariate, and quantifying uncer- tainties of all estimates. More importantly, we define a causal effect of treatment, which can be estimated from the data and has a nice causal interpretation. We perform extensive simulation studies to evaluate the proposed BNP model. The simulations show that the proposed model can accurately estimate the treatment effect in semi-competing risks setup. We also implement the proposed BNP model on data from a brain cancer Phase II trial.

Authors who are presenting talks have a * after their name.

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