Pooled Multinomial Logistic Regression for Parametric G-computation in the Presence of Competing Events.

Authors

Lucas M Neuroth, Monica E Swilley-Martinez, Paul N Zivich, Jessie K Edwards

Published in

Epidemiology (Cambridge, Mass.). Volume 37. Issue 5. Pages 591-595. Sep 01, 2026. Epub May 01, 2026.

Abstract

Parametric 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.
Data 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.
Both 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.
Incorporating a pooled multinomial logit nuisance model for parametric g-computation simplifies estimation of total effects while accounting for right-censoring and competing events.

PMID:
42525475
Bibliographic data and abstract were imported from PubMed on 30 Jul 2026.

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