A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding
arxiv(2024)
摘要
In longitudinal observational studies with a time-to-event outcome, a common
objective in causal analysis is to estimate the causal survival curve under
hypothetical intervention scenarios within the study cohort. The g-formula is a
particularly useful tool for this analysis. To enhance the traditional
parametric g-formula approach, we developed a more adaptable Bayesian g-formula
estimator. This estimator facilitates both longitudinal predictive and causal
inference. It incorporates Bayesian additive regression trees in the modeling
of the time-evolving generative components, aiming to mitigate bias due to
model misspecification. Specifically, we introduce a more general class of
g-formulas for discrete survival data. These formulas can incorporate the
longitudinal balancing scores, which serve as an effective method for dimension
reduction and are vital when dealing with an expanding array of time-varying
confounders. The minimum sufficient formulation of these longitudinal balancing
scores is linked to the nature of treatment regimes, whether static or dynamic.
For each type of treatment regime, we provide posterior sampling algorithms,
which are grounded in the Bayesian additive regression trees framework. We have
conducted simulation studies to illustrate the empirical performance of our
proposed Bayesian g-formula estimators, and to compare them with existing
parametric estimators. We further demonstrate the practical utility of our
methods in real-world scenarios using data from the Yale New Haven Health
System's electronic health records.
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