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Updated: May 13, 2025
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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
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A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding.
Xinyuan Chen1, Liangyuan Hu2, Fan Li3
1Department of Mathematics and Statistics, Mississippi State University, Mississippi State, MS, USA. xchen@math.msstate.edu.
Lifetime Data Analysis
|April 14, 2025
Summary
This study introduces an enhanced g-formula using Bayesian Additive Regression Trees to improve causal survival curve estimation in longitudinal studies. The method reduces bias from model misspecification for time-varying treatments.
Area of Science:
- Causal inference
- Biostatistics
- Observational studies
Background:
- Estimating causal survival curves in longitudinal studies with time-to-event outcomes is crucial for understanding treatment effects.
- The traditional parametric g-formula is a common tool but can be susceptible to model misspecification bias.
Purpose of the Study:
- To develop an enhanced g-formula estimator that mitigates bias due to model misspecification.
- To incorporate Bayesian Additive Regression Trees (BART) for modeling time-evolving components in causal survival analysis.
Main Methods:
- Developed an alternative g-formula estimator using BART for time-evolving generative components.
- Introduced a general class of g-formulas for discrete survival data with longitudinal balancing scores.
- Provided posterior sampling algorithms for static and dynamic treatment strategies.
Main Results:
- Simulations demonstrated the empirical performance of the proposed BART-enhanced g-formula.
- The method showed practical utility in analyzing electronic health records data.
Conclusions:
- The BART-enhanced g-formula offers a robust approach to estimating causal survival curves in longitudinal observational studies.
- This method improves upon traditional g-formulas by reducing bias from model misspecification, particularly for time-varying treatments.
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