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Applying Beta‐Type Size Distributions To Healthcare Cost Regressions

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  • Andrew M. Jones
  • James Lomas
  • Nigel Rice

Abstract

SUMMARY This paper extends the literature on modelling healthcare cost data by applying the generalised beta of the second kind (GB2) distribution to English hospital inpatient cost data. A quasi‐experimental design, estimating models on a sub‐population of the data and evaluating performance on another sub‐population, is used to compare this distribution with its nested and limiting cases. While for these data the beta of the second kind (B2) distribution and generalised gamma (GG) distribution outperform the GB2, our results illustrate that the GB2 can be used as a device for choosing among competing parametric distributions for healthcare cost data. Copyright © 2013 John Wiley & Sons, Ltd.

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  • 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.
    • Handle: RePEc:wly:japmet:v:29:y:2014:i:4:p:649-670
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    2. Jones, A. & Lomas, J. & Rice, N., 2014. "Going Beyond the Mean in Healthcare Cost Regressions: a Comparison of Methods for Estimating the Full Conditional Distribution," Health, Econometrics and Data Group (HEDG) Working Papers 14/26, HEDG, c/o Department of Economics, University of York.
    3. Sriubaite, I. & Harris, A. & Jones, A.M. & Gabbe, B., 2020. "Economic Consequences of Road Traffic Injuries. Application of the Super Learner algorithm," Health, Econometrics and Data Group (HEDG) Working Papers 20/20, HEDG, c/o Department of Economics, University of York.
    4. Martin Karlsson & Yulong Wang & Nicolas R. Ziebarth, 2023. "Getting the Right Tail Right: Modeling Tails of Health Expenditure Distributions," NBER Working Papers 31444, National Bureau of Economic Research, Inc.
    5. Andrew M. Jones & James Lomas & Peter T. Moore & Nigel Rice, 2016. "A quasi-Monte-Carlo comparison of parametric and semiparametric regression methods for heavy-tailed and non-normal data: an application to healthcare costs," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 951-974, October.
    6. Kasteridis, Panagiotis & Rice, Nigel & Santos, Rita, 2022. "Heterogeneity in end of life health care expenditure trajectory profiles," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 221-251.
    7. Peter Zweifel, 2012. "The Grossman model after 40 years," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 13(6), pages 677-682, December.
    8. Joel Smith & Helen Banks & Harry Campbell & Anne Douglas & Eilidh Fletcher & Alison McCallum & Tron Anders Moger & Mikko Peltola & Sofia Sveréus & Sarah Wild & Linda J. Williams & John Forbes & on beh, 2015. "Parameter Heterogeneity In Breast Cancer Cost Regressions – Evidence From Five European Countries," Health Economics, John Wiley & Sons, Ltd., vol. 24(S2), pages 23-37, December.
    9. 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.
    10. Sungchul Park & Anirban Basu, 2018. "Alternative evaluation metrics for risk adjustment methods," Health Economics, John Wiley & Sons, Ltd., vol. 27(6), pages 984-1010, June.
    11. Andrew M. Jones & James Lomas & Nigel Rice, 2015. "Healthcare Cost Regressions: Going Beyond the Mean to Estimate the Full Distribution," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1192-1212, September.
    12. Duangkamon Chotikapanich & William E. Griffiths & Gholamreza Hajargasht & Wasana Karunarathne & D.S. Prasada Rao, 2018. "Using the GB2 Income Distribution: A Review," Department of Economics - Working Papers Series 2036, The University of Melbourne.
    13. Piotr Swierkowski & Adrian Barnett, 2018. "Identification of hospital cost drivers using sparse group lasso," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.
    14. Julie Shi & Yi Yao & Gordon Liu, 2018. "Modeling individual health care expenditures in China: Evidence to assist payment reform in public insurance," Health Economics, John Wiley & Sons, Ltd., vol. 27(12), pages 1945-1962, December.
    15. Caravaggio, Nicola & Resce, Giuliano, 2023. "Enhancing Healthcare Cost Forecasting: A Machine Learning Model for Resource Allocation in Heterogeneous Regions," Economics & Statistics Discussion Papers esdp23090, University of Molise, Department of Economics.
    16. Tor Iversen & Eline Aas & Gunnar Rosenqvist & Unto Häkkinen & on behalf of the EuroHOPE study group, 2015. "Comparative Analysis of Treatment Costs in EUROHOPE," Health Economics, John Wiley & Sons, Ltd., vol. 24(S2), pages 5-22, December.
    17. Yi Yao & Joan Schmit & Julie Shi, 2019. "Promoting sustainability for micro health insurance: a risk-adjusted subsidy approach for maternal healthcare service," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 44(3), pages 382-409, July.
    18. Erengul Dodd & George Streftaris, 2017. "Prediction of settlement delay in critical illness insurance claims by using the generalized beta of the second kind distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(2), pages 273-294, February.
    19. Duangkamon Chotikapanich & William E. Griffiths & Gholamreza Hajargasht & Wasana Karunarathne & D. S. Prasada Rao, 2018. "Using the GB2 Income Distribution," Econometrics, MDPI, vol. 6(2), pages 1-24, April.

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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