ArticleAnnals of epidemiology2024
Semiparametric g-computation for survival outcomes with time-fixed exposures: An illustration.
Article in Annals of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Pooled Multinomial Logistic Regression for Parametric G-computation in the Presence of Competing Events.Epidemiology (Cambridge, Mass.) · 2026Article
- An Improved Pooled Logistic Regression Implementation.Epidemiology (Cambridge, Mass.) · 2026Article
- Mind the Gap: Addressing Missing Person Time When Estimating Outcome Incidence in Longitudinal Data.Epidemiology (Cambridge, Mass.) · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
purposeGeneralized (g-) computation is a useful tool for causal inference in epidemiology. However, in settings when the outcome is a survival time subject to right censoring, the standard pooled logistic regression approach to g-computation requires arbitrary discretization of time, parametric modeling of the baseline hazard function, and the need to expand one's dataset. We illustrate a semiparametric Breslow estimator for g-computation with time-fixed treatments and survival outcomes that is not subject to these limitations.
methodsWe compare performance of the Breslow g-computation estimator to the pooled logistic g-computation estimator in simulations and illustrate both approaches to estimate the effect of a 3-drug vs 2-drug antiretroviral therapy regimen among people with HIV.
resultsIn simulations, both approaches performed well at the end of follow-up. The pooled logistic approach was biased at times between the endpoints of the discrete time intervals used, while the Breslow approach was not. In the example, both approaches estimated a 1-year risk difference of about 6 % in favor of the 3-drug regimen, but the shape of the survival curves differed.
conclusionsThe Breslow g-computation estimator of counterfactual risk functions does not rely on strong parametric assumptions about the time-to-event distribution or onerous dataset expansions.
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