In this paper, we propose an innovative method for jointly analyzing survival data and longitudinally measured continuous and ordinal data. We use a random effects accelerated failure time model for survival outcomes, a linear mixed model for continuous longitudinal outcomes and a proportional odds mixed model for ordinal longitudinal outcomes, where these outcome processes are linked through a set of association parameters. A primary objective of this study is to examine the effects of association parameters on the estimators of joint models. The model parameters are estimated by the method of maximum likelihood. The finite-sample properties of the estimators are studied using Monte Carlo simulations. The empirical study suggests that the degree of association among the outcome processes influences the bias, efficiency, and coverage probability of the estimators. Our proposed joint model estimators are approximately unbiased and produce smaller mean squared errors as compared to the estimators obtained from separate models. This work is motivated by a large multicenter study, referred to as the Genetic and Inflammatory Markers of Sepsis (GenIMS) study. We apply our proposed method to the GenIMS data analysis.

在本文中，我们提出了一种联合分析生存数据和纵向测量的连续和有序数据的创新方法。我们对生存结果使用随机效应加速故障时间模型，对连续纵向结果使用线性混合模型，对有序纵向结果使用比例赔率混合模型，其中这些结果过程通过一组关联参数联系起来。本研究的主要目的是检查关联参数对联合模型估计量的影响。模型参数通过最大似然法估计。使用蒙特卡罗模拟研究估计器的有限样本特性。实证研究表明，结果过程之间的关联程度会影响偏差、效率、和估计量的覆盖概率。与从单独模型获得的估计量相比，我们提出的联合模型估计量近似无偏并且产生更小的均方误差。这项工作的动机是一项大型多中心研究，称为败血症的遗传和炎症标志物 (GenIMS) 研究。我们将我们提出的方法应用于 GenIMS 数据分析。