PreprintArticleVersion 1This version is not peer-reviewed
Comparing the relative efficacy of Generalized Estimating Equations, Latent Growth Curve Modeling, and Area Under the Curve with a repeated measures discrete ordinal outcome variable
Version 1
: Received: 4 October 2024 / Approved: 4 October 2024 / Online: 4 October 2024 (16:30:56 CEST)
How to cite:
Rodriguez, D.; Verma, R.; Upchurch, J. Comparing the relative efficacy of Generalized Estimating Equations, Latent Growth Curve Modeling, and Area Under the Curve with a repeated measures discrete ordinal outcome variable. Preprints2024, 2024100358. https://doi.org/10.20944/preprints202410.0358.v1
Rodriguez, D.; Verma, R.; Upchurch, J. Comparing the relative efficacy of Generalized Estimating Equations, Latent Growth Curve Modeling, and Area Under the Curve with a repeated measures discrete ordinal outcome variable. Preprints 2024, 2024100358. https://doi.org/10.20944/preprints202410.0358.v1
Rodriguez, D.; Verma, R.; Upchurch, J. Comparing the relative efficacy of Generalized Estimating Equations, Latent Growth Curve Modeling, and Area Under the Curve with a repeated measures discrete ordinal outcome variable. Preprints2024, 2024100358. https://doi.org/10.20944/preprints202410.0358.v1
APA Style
Rodriguez, D., Verma, R., & Upchurch, J. (2024). Comparing the relative efficacy of Generalized Estimating Equations, Latent Growth Curve Modeling, and Area Under the Curve with a repeated measures discrete ordinal outcome variable. Preprints. https://doi.org/10.20944/preprints202410.0358.v1
Chicago/Turabian Style
Rodriguez, D., Ryan Verma and Juliana Upchurch. 2024 "Comparing the relative efficacy of Generalized Estimating Equations, Latent Growth Curve Modeling, and Area Under the Curve with a repeated measures discrete ordinal outcome variable" Preprints. https://doi.org/10.20944/preprints202410.0358.v1
Abstract
Researchers are often interested in the effects of change in one variable on change in a second variable, requiring the repeated measures of two variables. There are several multivariate statistical methods appropriate for this research design, including generalized estimating equations (GEE) and latent growth curve modeling (LGCM). Both methods allow for variables that are not continuous in measurement level and not normally distributed. More recently, researchers have begun to employ area under the curve (AUC) as a viable alternative when the nature of change is less important than the overall effect of time on repeated measures of a random variable. The research showed that AUC is an acceptable alternative to LGCM with repeated measures of a continuous and a zero-inflated Poisson random variable. However, less is known about its performance relative to GEE and LGCM when the repeated measures are ordinal random variables. Further, no study to our knowledge has compared AUC to LGCM or GEE when there are two longitudinal processes. We thus compared AUC to LGCM and GEE, assessing the effects of repeated measures of psychological distress on repeated measures of smoking. Results suggest AUC performed equally well to both methods, although missing data management is an issue with both AUC and GEE.
Keywords
Area Under the Curve (AUC); Latent Growth Curve Modeling (LGCM); Generalized Estimating Equations (GEE)
Subject
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
