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Parametric models for biomarkers based on flexible size distributions

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  • Apostolos Davillas
  • Andrew M. Jones

Abstract

Recent advances in social science surveys include collection of biological samples. Although biomarkers offer a large potential for social science and economic research, they impose a number of statistical challenges, often being distributed asymmetrically with heavy tails. Using data from the UK Household Panel Survey, we illustrate the comparative performance of a set of flexible parametric distributions, which allow for a wide range of skewness and kurtosis: the four‐parameter generalized beta of the second kind (GB2), the three‐parameter generalized gamma, and their three‐, two‐, or one‐parameter nested and limiting cases. Commonly used blood‐based biomarkers for inflammation, diabetes, cholesterol, and stress‐related hormones are modelled. Although some of the three‐parameter distributions nested within the GB2 outperform the latter for most of the biomarkers considered, the GB2 can be used as a guide for choosing among competing parametric distributions for biomarkers. Going “beyond the mean” to estimate tail probabilities, we find that GB2 performs fairly well with some disparities at the very high levels of glycated hemoglobin and fibrinogen. Commonly used linear models are shown to perform worse than almost all the flexible distributions.

Suggested Citation

  • Apostolos Davillas & Andrew M. Jones, 2018. "Parametric models for biomarkers based on flexible size distributions," Health Economics, John Wiley & Sons, Ltd., vol. 27(10), pages 1617-1624, October.
  • Handle: RePEc:wly:hlthec:v:27:y:2018:i:10:p:1617-1624
    DOI: 10.1002/hec.3787
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    References listed on IDEAS

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    1. Andrew M. Jones & James Lomas & Nigel Rice, 2014. "Applying Beta‐Type Size Distributions To Healthcare Cost Regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(4), pages 649-670, June.
    2. Gruenewald, Tara L. & Cohen, Sheldon & Matthews, Karen A. & Tracy, Russell & Seeman, Teresa E., 2009. "Association of socioeconomic status with inflammation markers in black and white men and women in the Coronary Artery Risk Development in Young Adults (CARDIA) study," Social Science & Medicine, Elsevier, vol. 69(3), pages 451-459, August.
    3. Hendrik Jürges & Eberhard Kruk & Steffen Reinhold, 2013. "The effect of compulsory schooling on health—evidence from biomarkers," Journal of Population Economics, Springer;European Society for Population Economics, vol. 26(2), pages 645-672, April.
    4. Jones, Andrew M., 2017. "Data Visualization and Health Econometrics," Foundations and Trends(R) in Econometrics, now publishers, vol. 9(1), pages 1-78, August.
    5. Manning, Willard G. & Basu, Anirban & Mullahy, John, 2005. "Generalized modeling approaches to risk adjustment of skewed outcomes data," Journal of Health Economics, Elsevier, vol. 24(3), pages 465-488, May.
    6. Vincenzo Carrieri & Andrew M. Jones, 2017. "The Income–Health Relationship ‘Beyond the Mean’: New Evidence from Biomarkers," Health Economics, John Wiley & Sons, Ltd., vol. 26(7), pages 937-956, July.
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    Cited by:

    1. Andrew M. Jones, 2019. "Equity, opportunity and health," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(3), pages 413-421, August.

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    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality

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