ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2021
Bayesian semi-parametric G-computation for causal inference in a cohort study with MNAR dropout and death.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Long-term memory effects of an incremental blood pressure intervention in a mortal cohort.Biometrics · 2026Article
- A Bayesian semi-parametric approach to causal mediation for longitudinal mediators and time-to-event outcomes with application to a cardiovascular disease cohort study.Biostatistics (Oxford, England) · 2025Article
- A Bayesian Approach to the G-Formula via Iterative Conditional Regression.Statistics in medicine · 2025Article
- A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding.Lifetime data analysis · 2025Article
- Bayesian causal inference for observational studies with missingness in covariates and outcomes.Biometrics · 2023Article
- Parametric G-computation for compatible indirect treatment comparisons with limited individual patient data.Research synthesis methods · 2022Article
- Association of Cardiovascular Risk Trajectory With Cognitive Decline and Incident Dementia.Neurology · 2022Article
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Abstract
Causal inference with observational longitudinal data and time-varying exposures is often complicated by time-dependent confounding and attrition. The G-computation formula is one approach for estimating a causal effect in this setting. The parametric modeling approach typically used in practice relies on strong modeling assumptions for valid inference, and moreover depends on an assumption of missing at random, which is not appropriate when the missingness is missing not at random (MNAR) or due to death. In this work we develop a flexible Bayesian semi-parametric G-computation approach for assessing the causal effect on the subpopulation that would survive irrespective of exposure, in a setting with MNAR dropout. The approach is to specify models for the observed data using Bayesian additive regression trees, and then use assumptions with embedded sensitivity parameters to identify and estimate the causal effect. The proposed approach is motivated by a longitudinal cohort study on cognition, health, and aging, and we apply our approach to study the effect of becoming a widow on memory. We also compare our approach to several standard methods.
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