Robust Estimation under Outcome Dependent Right Censoring in Huntington Disease: Estimators for Low and High Censoring Rates

Across health applications, researchers model outcomes as a function of time to an event, but the event time is right-censored for participants who exit the study or otherwise do not experience the event during follow-up. When censoring depends on the outcome-as in neurodegenerative disease studies where dropout is potentially related to disease severity-standard regression estimators produce biased estimates. We develop three consistent estimators for this outcome-dependent censoring setting: two augmented inverse probability weighted (AIPW) estimators and one maximum likelihood estimator (MLE). We establish their asymptotic properties and derive their robust sandwich variance estimators that account for nuisance parameter estimation. A key contribution is demonstrating that the choice of estimator to use depends on the censoring rate-the MLE performs best under low censoring rates, while the AIPW estimators yield lower bias and a higher nominal coverage under high censoring rates. We apply our estimators to Huntington disease data to characterize health decline leading up to mild cognitive impairment onset. The AIPW estimator with robustness matrix provided clinically-backed estimates with improved precision over inverse probability weighting, while MLE exhibited bias. Our results provide practical guidance for estimator selection based on censoring rate.