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.

翻译：在健康研究应用中，研究者常将结局建模为事件发生时间的函数，但对于在研究期间退出或随访期内未经历事件的参与者，其事件时间存在右删失。当删失依赖于结局时——例如在神经退行性疾病研究中，退出可能与疾病严重程度相关——标准回归估计量会产生有偏估计。针对这种结果依赖的删失场景，我们开发了三种一致估计量：两种增强逆概率加权（AIPW）估计量及一种最大似然估计量（MLE）。我们建立了它们的渐近性质，并推导了考虑冗余参数估计的稳健三明治方差估计量。一个关键贡献在于证明估计量的选择取决于删失率：MLE在低删失率下表现最优，而AIPW估计量在高删失率下具有更低的偏差和更高的名义覆盖度。我们将这些估计量应用于亨廷顿病数据，以刻画轻度认知障碍发病前的健康衰退轨迹。具有稳健矩阵的AIPW估计量提供了临床支持的估计结果，其精度优于逆概率加权方法，而MLE则表现出偏差。我们的研究结果为基于删失率选择估计量提供了实用指导。