A SAS macro for the joint modeling of longitudinal outcomes and multiple competing risk dropouts

Long-term longitudinal studies often encounter data attrition because subjects may drop out or die before the study ends. Informative dropout due to competing risks is a critical aspect that should be taken into account when analyzing these data. For example, in the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS), studying risk factors for cognitive decline is a primary interest [1,2]. Cognitive function assessments were repeatedly measured on available participants over a 20 year study period, but many participants' cognitive function measurements were unavailable due to preceding dementia and/or death.

Source: Computer Methods and Programs in Biomedicine - October 17, 2016 Category: Bioinformatics Authors: Wei Wang, Wanmei Wang, Thomas H. Mosley, Michael E. Griswold Source Type: research