Zeng Donglin, Mao Lu, Lin D Y
Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A. ,
Biometrika. 2016 Jun;103(2):253-271. doi: 10.1093/biomet/asw013. Epub 2016 May 24.
Interval censoring arises frequently in clinical, epidemiological, financial and sociological studies, where the event or failure of interest is known only to occur within an interval induced by periodic monitoring. We formulate the effects of potentially time-dependent covariates on the interval-censored failure time through a broad class of semiparametric transformation models that encompasses proportional hazards and proportional odds models. We consider nonparametric maximum likelihood estimation for this class of models with an arbitrary number of monitoring times for each subject. We devise an EM-type algorithm that converges stably, even in the presence of time-dependent covariates, and show that the estimators for the regression parameters are consistent, asymptotically normal, and asymptotically efficient with an easily estimated covariance matrix. Finally, we demonstrate the performance of our procedures through simulation studies and application to an HIV/AIDS study conducted in Thailand.
区间删失在临床、流行病学、金融和社会学研究中经常出现，在这些研究中，仅知道感兴趣的事件或失败发生在定期监测所诱导的区间内。我们通过一类广泛的半参数变换模型来阐述潜在的随时间变化的协变量对区间删失失效时间的影响，这类模型包括比例风险模型和比例优势模型。对于这类模型，我们考虑每个受试者有任意数量监测时间的非参数最大似然估计。我们设计了一种即使在存在随时间变化的协变量时也能稳定收敛的期望最大化（EM）型算法，并表明回归参数的估计量是一致的、渐近正态的，且具有易于估计的协方差矩阵，是渐近有效的。最后，我们通过模拟研究以及应用于在泰国进行的一项艾滋病毒/艾滋病研究来展示我们方法的性能。
