文章基本信息

标题：JMFit: A SAS Macro for Joint Models of Longitudinal and Survival Data
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作者：Danjie Zhang ; Ming-Hui Chen ; Joseph G. Ibrahim 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2016
卷号：71
期号：1
页码：1-24
语种：English
出版社：University of California, Los Angeles
摘要：Joint models for longitudinal and survival data now have a long history of being used in clinical trials or other studies in which the goal is to assess a treatment effect while accounting for a longitudinal biomarker such as patient-reported outcomes or immune responses. Although software has been developed for fitting the joint model, no software packages are currently available for simultaneously fitting the joint model and assessing the fit of the longitudinal component and the survival component of the model separately as well as the contribution of the longitudinal data to the fit of the survival model. To fulfill this need, we develop a SAS macro, called JMFit. JMFit implements a variety of popular joint models and provides several model assessment measures including the decomposition of AIC and BIC as well as ∆AIC and ∆BIC recently developed in Zhang, Chen, Ibrahim, Boye, Wang, and Shen (2014). Examples with real and simulated data are provided to illustrate the use of JMFit.
关键词：AIC;BIC;patient-reported outcome (PRO);shared parameter model;time-varying covariates