TY - JOUR
T1 - A Comparison of Mean-Based and Quantile Regression Methods for Analyzing Self-Report Dietary Intake Data
AU - Vidoni, Michelle L.
AU - Reininger, Belinda M.
AU - Lee, Min Jae
N1 - Funding Information:
The authors would like to acknowledge and thank Dr. Belinda Reininger for her guidance and support on demonstrating the statistical approaches through the behavioral data observed from the intervention program, the Tu Salud ¡Si Cuenta! (Your Health Matters!) at Home Intervention, which was supported by the UT Health Clinical and Translational Science Award (UL1 TR000371), NIH/National Institute on Minority Health and Health Disparities (MD000170 P20), and the Texas Department of State Health Services funding for University of Texas Community Outreach (UTCO). The authors would like to recognize the support provided by the Biostatistics/Epidemiology/Research Design (BERD) component of the Center for Clinical and Translational Sciences (CCTS) at the UT Health Science Center at Houston, which is mainly funded by the NIH Centers for Translational Science Award (NIH CTSA), grant UL1 RR024148.
Publisher Copyright:
© 2019 Michelle L. Vidoni et al.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85063221311&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063221311&partnerID=8YFLogxK
U2 - 10.1155/2019/9750538
DO - 10.1155/2019/9750538
M3 - Review article
AN - SCOPUS:85063221311
SN - 1687-952X
VL - 2019
JO - Journal of Probability and Statistics
JF - Journal of Probability and Statistics
M1 - 9750538
ER -