论文标题
用间隔审核的多状态数据的半参数回归模型的最大似然估计
Maximum Likelihood Estimation for Semiparametric Regression Models with Interval-Censored Multi-State Data
论文作者
论文摘要
在许多慢性疾病的研究中出现了间隔审查的多状态数据，其中受试者的健康状况可以以有限的疾病状态为特征，并且任何两种状态之间的过渡仅在很大的时间间隔内发生。我们通过具有随机效应的半参数比例强度模型来制定潜在的时间依赖性协变量对多状态过程的影响。我们在一般间隔审查下采用非参数最大似然估计（NPMLE），并产生稳定的期望最大化（EM）算法。我们表明，所得的参数估计器是一致的，并且有限维成分渐近地正常，其协方差矩阵达到了半参数效率结合，并且可以通过轮廓可能性始终如一地估计。此外，我们通过广泛的仿真研究证明了所提出的数值和推论程序在现实环境中表现良好。最后，我们为主要的流行病学队列研究提供了应用。
Interval-censored multi-state data arise in many studies of chronic diseases, where the health status of a subject can be characterized by a finite number of disease states and the transition between any two states is only known to occur over a broad time interval. We formulate the effects of potentially time-dependent covariates on multi-state processes through semiparametric proportional intensity models with random effects. We adopt nonparametric maximum likelihood estimation (NPMLE) under general interval censoring and develop a stable expectation-maximization (EM) algorithm. We show that the resulting parameter estimators are consistent and that the finite-dimensional components are asymptotically normal with a covariance matrix that attains the semiparametric efficiency bound and can be consistently estimated through profile likelihood. In addition, we demonstrate through extensive simulation studies that the proposed numerical and inferential procedures perform well in realistic settings. Finally, we provide an application to a major epidemiologic cohort study.
