A0663
Title: A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding
Authors: Liangyuan Hu - Rutgers University (United States) [presenting]
Abstract: In longitudinal observational studies with time-to-event outcomes, a common objective in causal analysis is to estimate the causal survival curve under hypothetical intervention scenarios. The g-formula is a useful tool for this analysis. To enhance the traditional parametric g-formula, an alternative g-formula estimator is developed, which incorporates the Bayesian additive regression trees (BART) into the modeling of the time-evolving generative components, aiming to mitigate the bias due to model misspecification. The focus is on binary time-varying treatments, and a general class of g-formulas is introduced for discrete survival data that can incorporate longitudinal balancing scores. The minimum sufficient formulation of these longitudinal balancing scores is linked to the nature of treatment strategies, i.e., static or dynamic. For each type of treatment strategy, posterior sampling algorithms are provided. Simulations are conducted to illustrate the empirical performance of the proposed method, and its practical utility is demonstrated using data from the Yale New Haven Health System's electronic health records.
