Segmented mixed models with random changepoints: a maximum likelihood approach with application to treatment for depression study. (August 2014)

• Record Type: Journal Article
• Authors: Muggeo, Vito MR; Atkins, David C; Gallop, Robert J; Dimidjian, Sona
• Is Part Of: Statistical modelling. Volume 14:Number 4(2014)
• Page Start: 293
• Page End: 313
• Publication Date: 2014-08
• Subjects: changepoint -- mixed segmented regression -- nonlinear mixed models -- random changepoints -- psychiatric longitudinal data; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X13504721
• Languages: English
• ISSNs: 1471-082X

Abstract

We present a simple and effective iterative procedure to estimate segmented mixed models in 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 related to estimation of nonlinear mixed effects models. A conventional linear mixed model with proper covariates that account for the changepoints is the key to our estimating algorithm. We illustrate the method via simulations and using data from a randomized clinical trial focused on change in depressive symptoms over time which characteristically show two separate phases of change.