title: Identifying multiple change points in a linear mixed effects model.
abstract: Although change-point analysis methods for longitudinal data have been developed, it is often of interest to detect multiple change points in longitudinal data. In this paper, we propose a linear mixed effects modeling framework for identifying multiple change points in longitudinal Gaussian data. Specifically, we develop a novel statistical and computational framework that integrates the expectation-maximization and the dynamic programming algorithms. We conduct a comprehensive simulation study to demonstrate the performance of our method. We illustrate our method with an analysis of data from a trial evaluating a behavioral intervention for the control of type I diabetes in adolescents with HbA1c as the longitudinal response variable.
authors.affiliation: Department of Statistics and Biostatistics Center, The George Washington University, Rome Hall, Room 553, 801 22nd St. N.W., Washington, D.C., 20052, U.S.A.
authors.forename: Yinglei
authors.initials: Y
authors.lastname: Lai
authors.forename: Paul S
authors.initials: PS
authors.lastname: Albert
mesh_terms: D000293:Adolescent; D000465:Algorithms; D001521:Behavior Therapy; D056808:Biostatistics; D002648:Child; D003198:Computer Simulation; D016001:Confidence Intervals; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000523:psychology / Q000628:therapy; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data
chemical_list: D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human
keywords: change point; dynamic programming algorithm; expectation-maximization algorithm; linear mixed effects model; longitudinal data