ArticleEpidemiology (Cambridge, Mass.)2026
Pooled Multinomial Logistic Regression for Parametric G-computation in the Presence of Competing Events.
Article in Epidemiology (Cambridge, Mass.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundParametric g-computation with competing events typically involves fitting multiple pooled logistic regression models. We outline an alternative approach based on fitting a single pooled multinomial logistic model.
methodsData from the Women's Interagency HIV Study (n = 1,164) were used to estimate the marginal 2-year risk of highly active antiretroviral therapy (HAART) initiation and AIDS/death before HAART initiation with two parametric g-computation approaches: multiple pooled logistic regression and pooled multinomial logistic regression. The total effect of historical injection drug use was estimated for both event types using the multinomial approach.
resultsBoth g-computation implementations produced identical results. The 2-year risk difference comparing a scenario where all participants had historical injection drug use to one with no historical injection drug use was -12.5% (-18.0%, -7.0%) for HAART initiation and 13.2% (6.8%, 19.7%) for AIDS/death.
conclusionsIncorporating a pooled multinomial logit nuisance model for parametric g-computation simplifies estimation of total effects while accounting for right-censoring and competing events.
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