A SAS macro for the joint modeling of longitudinal outcomes and multiple competing risk dropouts. (January 2017)

• Record Type: Journal Article
• Authors: Wang, Wei; Wang, Wanmei; Mosley, Thomas H.; Griswold, Michael E.
• Is Part Of: Computer methods and programs in biomedicine. Volume 138(2017)
• Page Start: 23
• Page End: 30
• Publication Date: 2017-01
• Subjects: Joint modeling -- Shared parameter model -- Longitudinal submodels -- Competing causes for dropout; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.10.003
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A SAS macro implementation of a shared parameter model for a longitudinal outcome and multiple cause-specific dropouts. The linear mixed effects submodel for the longidudinal outcome, with the random intercept and/or random slope specifications. The survival submodel allows up to three different competing causes for dropout. Simulation study shows the unbiased parameter estimates when informative dropout exists. Abstract: Background and objectives: The joint modeling of longitudinal and survival data to assess effects of multiple informative dropout mechanisms on longitudinal outcomes inference has received considerable attention during recent years; related statistical programs to apply these methods have been lacking. This paper provides a SAS macro implementation of a shared parameter model to accommodate the analysis of longitudinal outcomes in the presence of multiple competing survival/dropout events. Methods: In this macro, we assumed that the associations between the survival and the longitudinal submodels are linked through a set of shared random effects. The submodel for the longitudinal outcome takes the form of a linear mixed effects model, with specifications for the random intercept and/or random slope. The survival submodel allows up to three different competing causes for dropout, each allowing either an exponential or Weibull parametric baseline hazard function. In addition, information criterion fit statistics AIC and BIC are provided to assist with parametric baseline hazard function selection. Results: We illustrate the SAS Macro in a cognitive decline study sensitivity analysis using data from the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS). In addition, we also conduct a simulation study to show that the joint model provides unbiased parameter estimates when informative dropout exists compared against separate model approach which assumes missing at random dropout mechanisms. Conclusions: We have presented a SAS macro to implement a shared parameter model for a longitudinal outcome and multiple cause-specific dropouts and made the macro code freely available for download.