Segmented mixed models with random changepoints: a maximum likelihood approach with application to treatment for depression study

Vito Michele Rosario Muggeo, Robert J. Gallop, David C. Atkins, Sona Dimidjian

Risultato della ricerca: Articlepeer review

27 Citazioni (Scopus)

Abstract

We present a simple and effective iterative procedure to estimate segmented mixed modelsin a likelihood based framework. Random effects and covariates are allowed for each model parameter,including the changepoint. The method is practical and avoids the computational burdens relatedto estimation of nonlinear mixed effects models. A conventional linear mixed model with propercovariates that account for the changepoints is the key to our estimating algorithm. We illustratethe method via simulations and using data from a randomized clinical trial focused on change indepressive symptoms over time which characteristically show two separate phases of change.
Lingua originaleEnglish
pagine (da-a)293-313
Numero di pagine21
RivistaStatistical Modelling
Volume14
Stato di pubblicazionePublished - 2014

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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