A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding

Xinyuan Chen (), Liangyuan Hu () and Fan Li ()
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Xinyuan Chen: Mississippi State University
Liangyuan Hu: Rutgers School of Public Health
Fan Li: Yale School of Public Health

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2025, vol. 31, issue 2, No 6, 394-421

Abstract: 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, we developed an alternative g-formula estimator, which incorporates the Bayesian Additive Regression Trees 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 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.

Keywords: Bayesian additive regression trees; Causal inference; g-computation; Longitudinal balancing scores; Time-varying confounding; Time-varying treatment strategy (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10985-025-09652-3

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