Modern methods for longitudinal data analysis, capabilities, caveats and cautions.
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TL;DR: Comparison of the two major approaches for longitudinal data analysis in terms of model assumptions, model parameter interpretation, applicability and limitations, using both real and simulated data is focused on.
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Abstract: Longitudinal studies are used in mental health research and services studies. The dominant approaches for longitudinal data analysis are the generalized linear mixed-effects models (GLMM) and the weighted generalized estimating equations (WGEE). Although both classes of models have been extensively published and widely applied, differences between and limitations about these methods are not clearly delineated and well documented. Unfortunately, some of the differences and limitations carry significant implications for reporting, comparing and interpreting research findings. In this report, we review both major approaches for longitudinal data analysis and highlight their similarities and major differences. We focus on comparison of the two classes of models in terms of model assumptions, model parameter interpretation, applicability and limitations, using both real and simulated data. We discuss caveats and cautions when applying the two different approaches to real study data.
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Citations
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TL;DR: The statistical approaches that underlie these different procedures and their strengths and weaknesses when applied to fit correlated binary responses are described and illustrated by applying these procedures implemented in some popular software packages to simulated and real study data.
A new look at the difference between the GEE and the GLMM when modeling longitudinal count responses
TL;DR: In this article, two popular extensions of this model, generalized estimating equations (GEE) and the generalized linear mixed-effects model (GLMM), are examined for longitudinal data analysis and complement the existing literature on characterizing the relationship between the two dueling paradigms in this setting.
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Modeling longitudinal binomial responses: implications from two dueling paradigms
TL;DR: This article showed that GLMM induces artifacts in the marginal models at assessment times, making it inappropriate when applied to such responses from real study data, and the different interpretations of parameters resulting from the conceptual difference between the two modeling approaches also carry quite significant implications and ramifications with respect to data and power analyses.
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Power analysis for cluster randomized trials with binary outcomes modeled by generalized linear mixed-effects models
Tian Chen,N. Lu,J. Arora,Ira R. Katz,Robert M. Bossarte,Hua He,Yinglin Xia,Hui Zhang,Xin Tu,Xin Tu +9 more
TL;DR: A new approach is developed to estimate power for cluster randomized control trials when a binary response is modeled by the GLMM that is easy to implement and seems to work quite well, as assessed by simulation studies.
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Power analysis for clustered non-continuous responses in multicenter trials
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