JMfit: a SAS macro for assessing model fit in 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 ∆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
Keywords for this software
References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Cong Xu, Pantelis Z. Hadjipantelis, Jane-Ling Wang: Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM (2020) not zbMATH
- Agnieszka Król; Audrey Mauguen; Yassin Mazroui; Alexandre Laurent; Stefan Michiels; Virginie Rondeau: Tutorial in Joint Modeling and Prediction: A Statistical Software for Correlated Longitudinal Outcomes, Recurrent Events and a Terminal Event (2017) not zbMATH
- Danjie Zhang; Ming-Hui Chen; Joseph Ibrahim; Mark Boye; Wei Shen: JMFit: A SAS Macro for Joint Models of Longitudinal and Survival Data (2016) not zbMATH
- Joeng, Hee-Koung; Chen, Ming-Hui; Kang, Sangwook: Proportional exponentiated link transformed hazards (ELTH) models for discrete time survival data with application (2016)