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

Author

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  • Sara Gustavsson

    (Occupational and Environmental Medicine, Sahlgrenska University Hospital and Academy, University Of Gothenburg, Gothenburg SE-405 30, Sweden)

  • Björn Fagerberg

    (Wallenberg Laboratory, Sahlgrenska Center for Cardiovascular and Metabolic Research, Sahlgrenska University Hospital, Gothenburg SE-413 45, Sweden
    Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg SE-413 45, Sweden)

  • Gerd Sallsten

    (Occupational and Environmental Medicine, Sahlgrenska University Hospital and Academy, University Of Gothenburg, Gothenburg SE-405 30, Sweden)

  • Eva M. Andersson

    (Occupational and Environmental Medicine, Sahlgrenska University Hospital and Academy, University Of Gothenburg, Gothenburg SE-405 30, Sweden)

Abstract

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.

Suggested Citation

  • Sara Gustavsson & Björn Fagerberg & Gerd Sallsten & Eva M. Andersson, 2014. "Regression Models for Log-Normal Data: Comparing Different Methods for Quantifying the Association between Abdominal Adiposity and Biomarkers of Inflammation and Insulin Resistance," IJERPH, MDPI, vol. 11(4), pages 1-19, March.
  • Handle: RePEc:gam:jijerp:v:11:y:2014:i:4:p:3521-3539:d:34506
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    References listed on IDEAS

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    1. Kenny S. Crump, 1998. "On Summarizing Group Exposures in Risk Assessment: Is an Arithmetic Mean or a Geometric Mean More Appropriate?," Risk Analysis, John Wiley & Sons, vol. 18(3), pages 293-297, June.
    2. Rabindra Nath Das & Jeong-Soo Park, 2012. "Discrepancy in regression estimates between log-normal and gamma: some case studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(1), pages 97-111, March.
    3. Xiao‐Hua Zhou & Kevin T. Stroupe & William M. Tierney, 2001. "Regression analysis of health care charges with heteroscedasticity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 303-312.
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