JMFit: A SAS Macro for Joint Models of Longitudinal and Survival Data

• Activity Number: 394
• Type: Invited
• Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section for Statistical Programmers and Analysts
• Author(s): Mark Ernest Boye*
• Companies: Eli Lilly and Company
• Keywords: AIC ; BIC ; Patient-reported Outcomes (PRO) ; Shared random-effects model ; Time- varying covariates model ; Trajectory model

Abstract

Joint models for longitudinal and survival data now have a long history of being used to analyze data from 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 models, no software is currently available for simultaneously fitting and evaluating joint models. 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 et al. (2014). Examples with real and simulated data are provided to illustrate the use of JMFit.

Danjie Zhang, Ming-Hui Chen, Joseph G. Ibrahim, Mark E. Boye, Ping Wang and Wei Shen. Assessing model fit in joint models of longitudinal and survival data with applications to cancer clinical trials. Statistics in Medicine. Article first published online : 20 JUL 2014, DOI: 10.1002/sim.6269