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• W1983143923 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." @default.
• W1983143923 created "2016-06-24" @default.
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• W1983143923 date "2013-09-30" @default.
• W1983143923 modified "2023-09-23" @default.
• W1983143923 title "Identifying multiple change points in a linear mixed effects model" @default.
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• W1983143923 doi "https://doi.org/10.1002/sim.5996" @default.
• W1983143923 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3971951" @default.
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