title: Semiparametric Mixed Effect Model with Application to the Longitudinal Knee Osteoarthritis (OAK) Data.
abstract: Motivated by the study of the longitudinal development and progression of knee osteoarthritis (OA) over a 15-year period, this study developed non-parametric mixed-effect models for ordinal outcomes. A stochastic mixed-effect model was used to evaluate the similarity of trajectories associated with increasing disease severity of OA in both knees. Then, a non-parametric mixed-effects model, based on cubic B-splnes, was developed to characterize the unknown nonlinear trend of logits as a function of time1-order. A Markov Transition Model was developed to characterize the transitions among multi-states of knee OA. This newly developed approach allows more flexible functional dependence of the ordinal outcome, levels of increasing knee OA severity, on the covariates.
authors.affiliation: Center for Integrated Approaches to Complex Diseases (CIACD), Department of Epidemiology, School of Public Health, The University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Huiyong
authors.initials: H
authors.lastname: Zheng
authors.forename: Maryfran
authors.initials: M
authors.lastname: Sowers
authors.forename: Carrie
authors.initials: C
authors.lastname: Karvonen-Gutierrez
authors.forename: Jon A
authors.initials: JA
authors.lastname: Jacobson
authors.forename: John F
authors.initials: JF
authors.lastname: Randolph
authors.forename: Siobàn D
authors.initials: SD
authors.lastname: Harlow
keywords: Aging; Epidemiology; Knee Osteoarthritis (OAK); Longitudinal Ordinal Data; MBHMS; Semiparametric Mixed Effect Model
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