Regression Models for Log-Normal Data: Comparing Different Methods for Quantifying the Association between Abdominal Adiposity and Biomarkers of Inflammation and Insulin Resistance

Regression Models for Log-Normal Data: Comparing Different Methods for Quantifying the Association between Abdominal Adiposity and Biomarkers of Inflammation and Insulin Resistance

We compared six methods for regression on log-normal heteroscedastic data with respect to the estimated associations with explanatory factors (bias and standard error) and the estimated expected outcome (bias and confidence interval). Method comparisons were based on results from a simulation study,...

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Bibliographic Details
Main Authors: Sara Gustavsson, Björn Fagerberg, Gerd Sallsten, Eva M. Andersson
Format: Article
Language:English
Published: MDPI AG 2014-03-01
Series:International Journal of Environmental Research and Public Health
Subjects:
linear regression model
log-normal distribution
heteroscedasticity
biomarkers of inflammation
insulin resistance
simulation study
Online Access:http://www.mdpi.com/1660-4601/11/4/3521
Description
Summary:We compared six methods for regression on log-normal heteroscedastic data with respect to the estimated associations with explanatory factors (bias and standard error) and the estimated expected outcome (bias and confidence interval). Method comparisons were based on results from a simulation study, and also the estimation of the association between abdominal adiposity and two biomarkers; C-Reactive Protein (CRP) (inflammation marker,) and Insulin Resistance (HOMA-IR) (marker of insulin resistance). Five of the methods provide unbiased estimates of the associations and the expected outcome; two of them provide confidence intervals with correct coverage.
ISSN:1660-4601

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