A joint modeling and estimation method for multivariate longitudinal data with mixed types of responses to analyze physical activity data generated by accelerometers. (7th August 2017)

• Record Type: Journal Article
• Authors: Li, Haocheng; Zhang, Yukun; Carroll, Raymond J.; Keadle, Sarah Kozey; Sampson, Joshua N.; Matthews, Charles E.
• Is Part Of: Statistics in medicine. Volume 36:Number 25(2017)
• Page Start: 4028
• Page End: 4040
• Publication Date: 2017-08-07
• Subjects: accelerometers -- longitudinal data -- mixed effects model -- multivariate longitudinal data -- penalized quasi‐likelihood; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.7401
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : A mixed effect model is proposed to jointly analyze multivariate longitudinal data with continuous, proportion, count, and binary responses. The association of the variables is modeled through the correlation of random effects. We use a quasi‐likelihood type approximation for nonlinear variables and transform the proposed model into a multivariate linear mixed model framework for estimation and inference. Via an extension to the EM approach, an efficient algorithm is developed to fit the model. The method is applied to physical activity data, which uses a wearable accelerometer device to measure daily movement and energy expenditure information. Our approach is also evaluated by a simulation study.