JMFit: A SAS Macro for Joint Models of Longitudinal and Survival Data.
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 ΔAIC and ΔBIC recently developed in Zhanget al.(2014). Examples with real and simulated data are provided to illustrate the use of JMFit.
Gilead Sciences, Inc., 333 Lakeside Drive, Foster City, CA 94404, U.S.A., danjie.zhang@gilead.com.
Danjie
D
Zhang
Department of Statistics, University of Connecticut, 215 Glenbrook Road U-4120, Storrs, CT 06269, U.S.A., ming-hui.chen@uconn.edu.
Ming-Hui
MH
Chen
Department of Biostatistics, University of North Carolina, McGavran Greenberg Hall CB#7420, Chapel Hill, NC 27599, U.S.A., ibrahim@bios.unc.edu.
Joseph G
JG
Ibrahim
Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, U.S.A., boyema@lilly.com.
Mark E
ME
Boye
Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, U.S.A., shen_wei_x1@lilly.com.
Wei
W
Shen
AIC; BIC; Patient-reported outcome (PRO); Shared parameter model; Time-varying covariates