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March 2018 How Gaussian mixture models might miss detecting factors that impact growth patterns
Brianna C. Heggeseth, Nicholas P. Jewell
Ann. Appl. Stat. 12(1): 222-245 (March 2018). DOI: 10.1214/17-AOAS1066

Abstract

Longitudinal studies play a prominent role in biological, social, and behavioral sciences. Repeated measurements over time facilitate the study of an outcome level, how individuals change over time, and the factors that may impact either or both. A standard approach to modeling childhood growth over time is to use multilevel or mixed effects models to study factors that might play a role in the level and growth over time. However, there has been increased interest in using mixture models, which have inherent grouping structure to more flexibly explain heterogeneity in the longitudinal outcomes, to study growth patterns. While several possible model specifications can be used, these methods generally fail to explicitly group individuals by the shape of their growth pattern separate from level, and thus fail to shed light on the relationships between growth pattern and potential explanatory factors. We illustrate the weaknesses of these methods as they are currently being used. We also propose a pre-processing step that removes the outcome level to focus explicitly on shape, discuss its impact on estimation, and demonstrate its usefulness though a simulation study and with real longitudinal data.

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Brianna C. Heggeseth. Nicholas P. Jewell. "How Gaussian mixture models might miss detecting factors that impact growth patterns." Ann. Appl. Stat. 12 (1) 222 - 245, March 2018. https://doi.org/10.1214/17-AOAS1066

Information

Received: 1 July 2016; Revised: 1 May 2017; Published: March 2018
First available in Project Euclid: 9 March 2018

zbMATH: 06894705
MathSciNet: MR3773392
Digital Object Identifier: 10.1214/17-AOAS1066

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.12 • No. 1 • March 2018
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