使用贝叶斯方法进行随机效应模型的纵向排名和事件时间数据的联合建模。加速失效时间（AFT）模型可用于分析事件时间数据以估计协变量对生存时间加速/减速效应的影响。参数AFT模型需要确定事件时间分布。因此，我们假设时间变量是用Weibull AFT分布建模的。在许多现实应用中，确定适当的分布有困难。为避免这种限制，提出了几种半参数AFT模型，包括基于样条的模型。因此，我们提出了加速失效时间模型的灵活扩展。此外，考虑了通常的联合线性模型和联合部分线性模型，其中包含时间对纵向排名响应的非线性影响以及协变量对风险的非线性且时间依赖效应。还使用了可产生模型参数的贝叶斯方法。进行了一些模拟研究以估计所考虑模型的参数。该模型应用于一组接受手术的真实脑肿瘤患者数据集。分析数据的结果被呈现以表示该方法。 © 2023 John Wiley＆Sons Ltd.

Joint modeling of longitudinal rank and time-to-event data with random effects model using a Bayesian approach is presented. Accelerated failure time (AFT) models can be used for the analysis of time-to-event data to estimate the effects of covariates on acceleration/deceleration of the survival time. The parametric AFT models require determining the event time distribution. So, we suppose that the time variable is modeled with Weibull AFT distribution. In many real-life applications, it is difficult to determine the appropriate distribution. To avoid this restriction, several semiparametric AFT models were proposed, containing spline-based model. So, we propose a flexible extension of the accelerated failure time model. Furthermore, the usual joint linear model, a joint partially linear model, is also considered containing the nonlinear effect of time on the longitudinal rank responses and nonlinear and time-dependent effects of covariates on the hazard. Also, a Bayesian approach that yields Bayesian estimates of the model's parameters is used. Some simulation studies are conducted to estimate parameters of the considered models. The model is applied to a real brain tumor patient's data set that underwent surgery. The results of analyzing data are presented to represent the method.© 2023 John Wiley & Sons Ltd.