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

International Journal of Environmental Research and Public Health

Sara Gustavsson¹  Björn Fagerberg²  Gerd Sallsten¹ 

[1] Occupational and Environmental Medicine, Sahlgrenska University Hospital and Academy, University Of Gothenburg, Gothenburg SE-405 30, Sweden; E-Mails:;Wallenberg Laboratory, Sahlgrenska Center for Cardiovascular and Metabolic Research, Sahlgrenska University Hospital, Gothenburg SE-413 45, Sweden; E-Mail:

关键词: linear regression model;    log-normal distribution;    heteroscedasticity;    biomarkers of inflammation;    insulin resistance;    simulation study;   

DOI  :  10.3390/ijerph110403521

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.