This article considers the joint modeling of longitudinal covariates and partly-interval censored time-to-event data. Longitudinal time-varying covariates play a crucial role in obtaining accurate clinically relevant predictions using a survival regression model. However, these covariates are often measured at limited time points and may be subject to measurement error. Further methodological challenges arise from the fact that, in many clinical studies, the event times of interest are interval-censored. A model that simultaneously accounts for all these factors is expected to improve the accuracy of survival model estimations and predictions. In this article, we consider joint models that combine longitudinal time-varying covariates with the Cox model for time-to-event data which is subject to interval censoring. The proposed model employs a novel penalised likelihood approach for estimating all parameters, including the random effects. The covariance matrix of the estimated parameters can be obtained from the penalised log-likelihood. The performance of the model is compared to an existing method under various scenarios. The simulation results demonstrated that our new method can provide reliable inferences when dealing with interval-censored data. Data from the Anti-PD1 brain collaboration clinical trial in advanced melanoma is used to illustrate the application of the new method.

翻译：本文研究纵向协变量与部分区间删失生存时间的联合建模问题。在利用生存回归模型获取临床相关预测时，纵向时变协变量对提升预测精度具有关键作用。然而，这些协变量通常仅在有限时间点被测量，且可能受到测量误差的影响。此外，许多临床研究中目标事件时间存在区间删失现象，这带来了进一步的方法学挑战。能够同时处理所有这些因素的模型有望提高生存模型估计与预测的准确性。本文构建的联合模型将纵向时变协变量与适用于区间删失生存数据的Cox模型相结合。该模型采用一种新颖的惩罚似然方法估计所有参数（包括随机效应），其参数协方差矩阵可通过惩罚对数似然函数获得。通过多种情境下的模拟实验，将所提模型与现有方法进行比较。结果表明，在处理区间删失数据时，新方法能够提供可靠的统计推断。最后，以晚期黑色素瘤的Anti-PD1脑部协作临床试验数据为例，展示了新方法的应用价值。