The first‐order Markov conditional linear expectation approach for analysis of longitudinal data. (2nd February 2021)

• Record Type: Journal Article
• Authors: Bender, Shaun; Gamerman, Victoria; Reese, Peter P.; Gray, Daniel Lloyd; Li, Yimei; Shults, Justine
• Is Part Of: Statistics in medicine. Volume 40:Number 8(2021)
• Page Start: 1972
• Page End: 1988
• Publication Date: 2021-02-02
• Subjects: binary random variables -- discrete data -- first‐order antedependence -- first‐order Markov -- generalized estimating equations; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8883
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : We consider longitudinal discrete data that may be unequally spaced in time and may exhibit overdispersion, so that the variance of the outcome variable is inflated relative to its assumed distribution. We implement an approach that extends generalized linear models for analysis of longitudinal data and is likelihood based, in contrast to generalized estimating equations (GEE) that are semiparametric. The method assumes independence between subjects; first‐order antedependence within subjects; exponential family distributions for the first outcome on each subject and for the subsequent conditional distributions; and linearity of the expectations of the conditional distributions. We demonstrate application of the method in an analysis of seizure counts and in a study to evaluate the performance of transplant centers. Simulations for both studies demonstrate the benefits of the proposed likelihood based approach; however, they also demonstrate better than anticipated performance for GEE.