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B0581
Title: Bayesian non-parametric G-computation in the presence of non-ignorable dropout and death Authors:  Maria Josefsson - Centre for Demographic and Ageing Research (Sweden) [presenting]
Michael Daniels - University of Florida (United States)
Abstract: Causal inference with observational longitudinal data and time-varying exposures is often complicated by time-dependent confounding and attrition. G-computation is one method used for estimating a causal effect when time-varying confounding is present. The parametric modeling approach most often used in practice relies on strong modeling assumptions for valid inference, and moreover depends on an assumption of missing at random, which is generally invalid when the missingness is non-ignorable or due to death. We develop a flexible Bayesian non-parametric G-computation approach for assessing the causal effect on the subpopulation that would survive irrespective of exposure, in a setting with non-ignorable dropout. The approach is to specify models for the observed data using Bayesian additive regression trees, and then use assumptions with embedded sensitivity parameters to identify and estimate the causal effect. The proposed approach is motivated by a longitudinal cohort study on cognition, health, and ageing. We apply our approach to study the effect of becoming a widow on memory.