Abstract

Longitudinal count and time to event (TTE) data are often associated in some ways. Hence, using joint models for analyzing these data constitutes an attractive modeling framework which is applied in many different fields of statistics and clinical studies. Also, Accelerated Failure Time (AFT) models can be used for the analysis of TTE data to estimate the effects of covariates on acceleration/deceleration of the survival time. So, we assume that the time variable is modeled in this article with Weibull AFT distribution. Furthermore, to develop the joint modeling strategy of these kinds of data, a correlated generalized linear mixed effect model (GLMEM) is applied using a member of the family of power series (PS) distributions in the longitudinal count submodel. Both of longitudinal count and TTE data may have excess zeros. The adequate of this approach in joint modeling with considering two censoring mechanism, right and left is illustrated using some simulation studies. Finally, we implement these proposed joint models on real AIDS data set.

摘要

纵向计数和事件发生时间 (TTE) 数据通常以某种方式关联。因此，使用联合模型来分析这些数据构成了一个有吸引力的建模框架，该框架应用于许多不同的统计和临床研究领域。此外，加速失效时间 (AFT) 模型可用于分析 TTE 数据，以估计协变量对生存时间加速/减速的影响。因此，我们假设本文中的时间变量使用 Weibull AFT 分布建模。此外，为了开发此类数据的联合建模策略，使用纵向计数子模型中的幂级数 (PS) 分布族的成员应用相关广义线性混合效应模型 (GLMEM)。纵向计数和 TTE 数据都可能有多余的零。使用一些模拟研究说明了这种方法在考虑左右两种审查机制的联合建模中的充分性。最后，我们在真实的 AIDS 数据集上实现了这些提出的联合模型。