A Comparison of Mean-Based and Quantile Regression Methods for Analyzing Self-Report Dietary Intake Data
In mean-based approaches to dietary data analysis, it is possible for potentially important associations at the tails of the intake distribution, where inadequacy or excess is greatest, to be obscured due to unobserved heterogeneity. Participants in the upper or lower tails of dietary intake data wi...
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doaj-fd67144f14554581a89fe06e05df18802020-11-25T02:22:49ZengHindawi LimitedJournal of Probability and Statistics1687-952X1687-95382019-01-01201910.1155/2019/97505389750538A Comparison of Mean-Based and Quantile Regression Methods for Analyzing Self-Report Dietary Intake DataMichelle L. Vidoni0Belinda M. Reininger1MinJae Lee2Biostatistics, Epidemiology, and Research Design (BERD) Core, Center for Clinical and Translational Sciences (CCTS), The University of Texas Health Science Center at Houston, 6410 Fannin, Houston, TX 77030, USAHealth Promotion & Behavioral Sciences, Hispanic Health Research Center, The University of Texas School of Public Health Brownsville Regional Campus, One West University Blvd., Brownsville, TX 78520, USABiostatistics, Epidemiology, and Research Design (BERD) Core, Center for Clinical and Translational Sciences (CCTS), The University of Texas Health Science Center at Houston, 6410 Fannin, Houston, TX 77030, USAIn mean-based approaches to dietary data analysis, it is possible for potentially important associations at the tails of the intake distribution, where inadequacy or excess is greatest, to be obscured due to unobserved heterogeneity. Participants in the upper or lower tails of dietary intake data will potentially have the greatest change in their behavior when presented with a health behavior intervention; thus, alternative statistical methods to modeling these relationships are needed to fully describe the impact of the intervention. Using data from Tu Salud ¡Si Cuenta! (Your Health Matters!) at Home Intervention, we aimed to compare traditional mean-based regression to quantile regression for describing the impact of a health behavior intervention on healthy and unhealthy eating indices. The mean-based regression model identified no differences in dietary intake between intervention and standard care groups. In contrast, the quantile regression indicated a nonconstant relationship between the unhealthy eating index and study groups at the upper tail of the unhealthy eating index distribution. The traditional mean-based linear regression was unable to fully describe the intervention effect on healthy and unhealthy eating, resulting in a limited understanding of the association.http://dx.doi.org/10.1155/2019/9750538 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Michelle L. Vidoni Belinda M. Reininger MinJae Lee |
spellingShingle |
Michelle L. Vidoni Belinda M. Reininger MinJae Lee A Comparison of Mean-Based and Quantile Regression Methods for Analyzing Self-Report Dietary Intake Data Journal of Probability and Statistics |
author_facet |
Michelle L. Vidoni Belinda M. Reininger MinJae Lee |
author_sort |
Michelle L. Vidoni |
title |
A Comparison of Mean-Based and Quantile Regression Methods for Analyzing Self-Report Dietary Intake Data |
title_short |
A Comparison of Mean-Based and Quantile Regression Methods for Analyzing Self-Report Dietary Intake Data |
title_full |
A Comparison of Mean-Based and Quantile Regression Methods for Analyzing Self-Report Dietary Intake Data |
title_fullStr |
A Comparison of Mean-Based and Quantile Regression Methods for Analyzing Self-Report Dietary Intake Data |
title_full_unstemmed |
A Comparison of Mean-Based and Quantile Regression Methods for Analyzing Self-Report Dietary Intake Data |
title_sort |
comparison of mean-based and quantile regression methods for analyzing self-report dietary intake data |
publisher |
Hindawi Limited |
series |
Journal of Probability and Statistics |
issn |
1687-952X 1687-9538 |
publishDate |
2019-01-01 |
description |
In mean-based approaches to dietary data analysis, it is possible for potentially important associations at the tails of the intake distribution, where inadequacy or excess is greatest, to be obscured due to unobserved heterogeneity. Participants in the upper or lower tails of dietary intake data will potentially have the greatest change in their behavior when presented with a health behavior intervention; thus, alternative statistical methods to modeling these relationships are needed to fully describe the impact of the intervention. Using data from Tu Salud ¡Si Cuenta! (Your Health Matters!) at Home Intervention, we aimed to compare traditional mean-based regression to quantile regression for describing the impact of a health behavior intervention on healthy and unhealthy eating indices. The mean-based regression model identified no differences in dietary intake between intervention and standard care groups. In contrast, the quantile regression indicated a nonconstant relationship between the unhealthy eating index and study groups at the upper tail of the unhealthy eating index distribution. The traditional mean-based linear regression was unable to fully describe the intervention effect on healthy and unhealthy eating, resulting in a limited understanding of the association. |
url |
http://dx.doi.org/10.1155/2019/9750538 |
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