A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding
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Abstract (arXiv)
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, we developed an alternative g-formula estimator, 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. We focus on binary time-varying treatments and introduce a general class of g-formulas for discrete survival data that can incorporate the 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, we provide posterior sampling algorithms. We conducted simulations to illustrate the empirical performance of the proposed method and demonstrate its practical utility using data from the Yale New Haven Health System's electronic health records.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 75 references that are in the map. First posted on arXiv 4 Feb 2024.
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22 of 75 references.
References · 22
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10 more
- 2019Model interpretation through lower-dimensional posterior summarizationcited by 5
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- 2021Revisiting the g-null paradoxcited by 3
- 2021Addressing Positivity Violations in Causal Effect Estimation using Gaussian Process Priorscited by 3
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