|Title||JMFit: A SAS Macro for Joint Models of Longitudinal and Survival Data.|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Authors||Zhang, Danjie, Ming-Hui Chen, Joseph G. Ibrahim, Mark E. Boye, and Wei Shen|
|Journal||J Stat Softw|
|Date Published||2016 Jul|
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 Zhang (2014). Examples with real and simulated data are provided to illustrate the use of JMFit.
|Alternate Journal||J Stat Softw|
|Original Publication||JMFit: A SAS macro for joint models of longitudinal and survival data.|
|PubMed Central ID||PMC5015698|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 GM070335 / GM / NIGMS NIH HHS / United States
JMFit: A SAS Macro for Joint Models of Longitudinal and Survival Data.