title: A bivariate autoregressive linear mixed effects model for the analysis of longitudinal data.
abstract: In clinical studies, dependent bivariate continuous responses may approach equilibrium over time. We propose an autoregressive linear mixed effects model for bivariate longitudinal data in which the current responses are regressed on the previous responses of both variables, fixed effects, and random effects. The equilibria are modeled using fixed and random effects. This model is a bivariate extension of the model for univariate longitudinal data given by Funatogawa et al. (Statist. Med. 2007; 26:2113-2130). As an illustration of the approach we analyze parathyroid hormone and serum calcium measurements in the treatment of secondary hyperparathyroidism in chronic hemodialysis patients.
authors.affiliation: Department of Hygiene and Public Health, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-Ku, Tokyo 173-8605, Japan. ifunatogawa-tky@umin.ac.jp
authors.forename: Ikuko
authors.initials: I
authors.lastname: Funatogawa
authors.forename: Takashi
authors.initials: T
authors.lastname: Funatogawa
authors.forename: Yasuo
authors.initials: Y
authors.lastname: Ohashi
mesh_terms: D002117:Calcitriol / Q000008:administration & dosage / Q000031:analogs & derivatives; D002118:Calcium / Q000097:blood; D006801:Humans; D006962:Hyperparathyroidism, Secondary / Q000097:blood / Q000628:therapy; D016014:Linear Models; D008137:Longitudinal Studies; D010281:Parathyroid Hormone / Q000097:blood; D006435:Renal Dialysis; D014815:Vitamins / Q000008:administration & dosage
chemical_list: D010281:Parathyroid Hormone; D014815:Vitamins; D002117:Calcitriol; C051883:maxacalcitol; D002118:Calcium