Abstract

Methods for joint analysis of longitudinal measures and right-censored survival outcomes have received much attention in the literature. However, in clinic and epidemiology research, the event of interest is only examined at the inspection time, resulting in the interval-censored failure time data. In this article, we construct a joint model for the longitudinal outcomes and the failure time, which is subject to interval censoring. We account for the dependence between the longitudinal measures and failure time through the incorporation of random effects. The semiparametric regression model with random effects and the Cox frailty model is used for the longitudinal outcome and the failure time, respectively. Spline functions are applied to approximate the infinite-dimensional baseline mean function and baseline cumulative hazard function in the joint model. The EM algorithm is used to find the maximum likelihood estimators and the estimated standard deviations are computed from the log observed-data likelihood. Extensive simulation studies are conducted to assess the performance of the proposed estimators. Simulation results show that the proposed method performs quite well. Finally, we illustrate the proposed approach by analyzing the chronic kidney disease.

摘要

纵向测量和右删失生存结果的联合分析方法在文献中受到了广泛关注。然而，在临床和流行病学研究中，感兴趣的事件仅在检查时进行检查，导致间隔删失的故障时间数据。在本文中，我们为纵向结果和故障时间构建了一个联合模型，该模型受到区间审查。我们通过纳入随机效应来解释纵向测量和失效时间之间的依赖性。具有随机效应的半参数回归模型和 Cox 脆弱模型分别用于纵向结果和失效时间。样条函数用于逼近联合模型中的无限维基线均值函数和基线累积危险函数。EM 算法用于找到最大似然估计量，并且估计的标准偏差是根据对数观测数据似然计算的。进行了广泛的模拟研究以评估所提出的估计器的性能。仿真结果表明，所提出的方法表现良好。最后，我们通过分析慢性肾脏疾病来说明所提出的方法。仿真结果表明，所提出的方法表现良好。最后，我们通过分析慢性肾脏疾病来说明所提出的方法。仿真结果表明，所提出的方法表现良好。最后，我们通过分析慢性肾脏疾病来说明所提出的方法。