Sigmoidal mixed models for longitudinal data

Ana W Capuano; Robert S Wilson; Sue E Leurgans; Jeffrey D Dawson; David A Bennett; Donald Hedeker

doi:10.1177/0962280216645632

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Sigmoidal mixed models for longitudinal data

Journal article

Open access

Peer reviewed

Sigmoidal mixed models for longitudinal data

Ana W Capuano, Robert S Wilson, Sue E Leurgans, Jeffrey D Dawson, David A Bennett and Donald Hedeker

Statistical methods in medical research, Vol.27(3), pp.863-875

03/2018

DOI: 10.1177/0962280216645632

PMCID: PMC5503801

PMID: 27130491

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https://www.ncbi.nlm.nih.gov/pmc/articles/5503801View

Open Access

Abstract

Linear mixed models are widely used to analyze longitudinal cognitive data. Often, however, the trajectory of cognitive function is nonlinear. For example, some participants may experience cognitive decline that accelerates as death approaches. Polynomial regression and piecewise linear models are common approaches used to characterize nonlinear trajectories, although both have assumptions that may not correspond with the actual trajectories. An alternative is to use a flexible sigmoidal mixed model based on the logistic family of curves. We describe a general class of such a model, which has up to five parameters, representing (1) final level, (2) rate of decline, (3) midpoint of decline, (4) initial level before decline, and (5) asymmetry. Focusing on a four-parameter symmetric sub-class of the model, with random effects on two of the parameters, we demonstrate that a likelihood approach to fitting this model produces accurate estimates of mean levels across time, even in the case of model misspecification. We also illustrate the method on deceased participants who had completed at least 5 years of annual cognitive testing and annual assessment of body mass. We show that departures from a stable body can modify the trajectory curves and anticipate cognitive decline.

Nonlinear models

mixed models

Alzheimer’s disease

cognitive decline

terminal decline

longitudinal data

Details

Title: Subtitle: Sigmoidal mixed models for longitudinal data
Creators: Ana W Capuano - Department of Public Health Sciences, Biostatistics, The University of Chicago, Chicago, IL, USA
Robert S Wilson - Department of Public Health Sciences, Biostatistics, The University of Chicago, Chicago, IL, USA
Sue E Leurgans - Department of Public Health Sciences, Biostatistics, The University of Chicago, Chicago, IL, USA
Jeffrey D Dawson - Department of Public Health Sciences, Biostatistics, The University of Chicago, Chicago, IL, USA
David A Bennett - Department of Public Health Sciences, Biostatistics, The University of Chicago, Chicago, IL, USA
Donald Hedeker - Department of Public Health Sciences, Biostatistics, The University of Chicago, Chicago, IL, USA
Contributors: Christos T Nakas (Editor)
Benjamin Reiser (Editor)
Resource Type: Journal article
Publication Details: Statistical methods in medical research, Vol.27(3), pp.863-875
DOI: 10.1177/0962280216645632
PMID: 27130491
PMCID: PMC5503801
NLM abbreviation: Stat Methods Med Res
ISSN: 0962-2802
eISSN: 1477-0334
Publisher: SAGE Publications; London, England
Language: English
Date published: 03/2018
Academic Unit: Public Health Administration; Biostatistics; Geographical and Sustainability Sciences
Record Identifier: 9983997321602771

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