A censored multinomial regression model with application to perinatal mother to child transmission of HIV
Abstract
When estimating rates of perinatal mother to child transmission of HIV, HIV assays are scheduled at multiple points in time. Still infection status for some infants at some time points is unknown. Logistic regression and Cox proportional hazards regression are commonly used to estimate covariate-adjusted transmission rates, but their methods for handling missing data may be inadequate. Here, we propose using censored multinomial regression models to estimate cumulative and conditional rates of HIV transmission using both logit and complementary log-log links. Through simulation, we show that the proposed methods perform better than standard methods in terms of bias, mean square error, coverage probability, and power under a range of treatment effect and visit process scenarios.