Regression Models for Log-Normal Data
Comparing Different Methods for Quantifying the Association between Abdominal Adiposity and Biomarkers of Inflammation and Insulin Resistance
Bibliographic Data
| ID | 15464871 |
|---|---|
| Authors | Sara Gustavsson (0000-0001-7469-5851, Sahlgrenska University Hospital, corresponding author), Björn Fagerberg (0000-0003-0575-1375, Sahlgrenska University Hospital), Gerd Sällsten (Sahlgrenska University Hospital), Eva Andersson (0000-0002-2854-0354, Occupational and Environmental Medicine, Sahlgrenska University Hospital and Academy, University Of Gothenburg, Gothenburg SE-405 30, Sweden), Eva M Andersson (Sahlgrenska University Hospital) |
| Year | 2014 |
| Volume | 11 |
| Issue | 4 |
| Pages | 3521-3539 |
| Publication date | 2014-03-27 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph110403521 |
| PMID | 24681553 |
| PMCID | PMC4024993 |
| OpenAlex | W2037392125 |
| Language | EN |
| References cited | 55 |
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
Confidence interval · Heteroscedasticity · Insulin · Insulin resistance · Regression · Regression analysis · Standard error · Statistics · Adipokines, Inflammation, and Metabolic Diseases · Mathematics · Medicine · Nutritional Studies and Diet · Statistical Methods and Inference · Internal Medicine
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| Highly cited | No |
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