In studies designed to estimate 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 often unknown, particularly when interim analyses are conducted. 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. Through simulation, we show that the proposed methods perform better than standard logistic models in terms of bias, mean squared error, coverage probability, and power, under a range of treatment effect and visit process scenarios.


Categorical Data Analysis | Clinical Trials