Pooled multinomial logistic regression for parametric g-computation in the presence of competing events.

Background: 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. Methods: Data from the Women’s Interagency HIV Study (n=1,164) were used to estimate the marginal two-year risk of highly active antiretroviral therapy (HAART) initiation and AIDS/death prior to 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 mutinomial approach. Results: Both g-computation implementations produced identical results. The two-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. Conclusions: Incorporating a pooled multinomial logit nuisance model for parametric g-computation simplifies estimation of total effects while accounting for right-censoring and competing events.