Zeng Donglin, Gao Fei, Lin D Y
Department of Biostatistics, CB#7420, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A.
Biometrika. 2017 Sep;104(3):505-525. doi: 10.1093/biomet/asx029. Epub 2017 Jul 12.
Interval-censored multivariate failure time data arise when there are multiple types of failure or there is clustering of study subjects and each failure time is known only to lie in a certain interval. We investigate the effects of possibly time-dependent covariates on multivariate failure times by considering a broad class of semiparametric transformation models with random effects, and we study nonparametric maximum likelihood estimation under general interval-censoring schemes. We show that the proposed estimators for the finite-dimensional parameters are consistent and asymptotically normal, with a limiting covariance matrix that attains the semiparametric efficiency bound and can be consistently estimated through profile likelihood. In addition, we develop an EM algorithm that converges stably for arbitrary datasets. Finally, we assess the performance of the proposed methods in extensive simulation studies and illustrate their application using data derived from the Atherosclerosis Risk in Communities Study.
当存在多种类型的失败或研究对象存在聚类，且每个失败时间仅已知位于某个区间时，就会出现区间删失的多变量失败时间数据。我们通过考虑一类广泛的具有随机效应的半参数变换模型，研究可能随时间变化的协变量对多变量失败时间的影响，并在一般区间删失方案下研究非参数最大似然估计。我们表明，所提出的有限维参数估计量是一致的且渐近正态的，其极限协方差矩阵达到半参数效率界，并且可以通过轮廓似然进行一致估计。此外，我们开发了一种对任意数据集都能稳定收敛的期望最大化（EM）算法。最后，我们在广泛的模拟研究中评估所提出方法的性能，并使用社区动脉粥样硬化风险研究得出的数据说明它们的应用。
