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Parametric modelling of cost data: some simulation evidence

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Author Info

  • Andrew Briggs

    (University of Oxford, UK)

  • Richard Nixon

    (MRC Biostatistics Unit, Cambridge, UK)

  • Simon Dixon

    (University of Sheffield, UK)

  • Simon Thompson

    (MRC Biostatistics Unit, Cambridge, UK)

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    Abstract

    Recently, commentators have suggested that the distributional form of cost data should be explicitly modelled to gain efficiency in estimating the population mean. We perform a series of simulation experiments to evaluate the usual sample mean and the mean estimator of a lognormal distribution, in the context of both theoretical distributions and three large empirical datasets. The sample mean is always unbiased, but is somewhat less efficient when the population distribution is truly lognormal. However the lognormal estimator can perform appallingly when the true distribution is not lognormal. In practical situations, where the true distribution is unknown, the sample mean generally remains the estimator of choice, especially when limited sample size prohibits detailed modelling of the cost data distribution. Copyright © 2005 John Wiley & Sons, Ltd.

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    File URL: http://hdl.handle.net/10.1002/hec.941
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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

    Volume (Year): 14 (2005)
    Issue (Month): 4 ()
    Pages: 421-428

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    Handle: RePEc:wly:hlthec:v:14:y:2005:i:4:p:421-428

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    Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/5749

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    References

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    1. Willard G. Manning & John Mullahy, 1999. "Estimating Log Models: To Transform or Not to Transform?," NBER Technical Working Papers 0246, National Bureau of Economic Research, Inc.
    2. Mullahy, John, 1998. "Much ado about two: reconsidering retransformation and the two-part model in health econometrics," Journal of Health Economics, Elsevier, vol. 17(3), pages 247-281, June.
    3. Zhou, Xiao-Hua & Tu, Wanzhu, 2000. "Interval estimation for the ratio in means of log-normally distributed medical costs with zero values," Computational Statistics & Data Analysis, Elsevier, vol. 35(2), pages 201-210, December.
    4. Manning, Willard G, et al, 1987. "Health Insurance and the Demand for Medical Care: Evidence from a Randomized Experiment," American Economic Review, American Economic Association, vol. 77(3), pages 251-77, June.
    5. Duan, Naihua, et al, 1983. "A Comparison of Alternative Models for the Demand for Medical Care," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 115-26, April.
    6. Partha Deb & James F. Burgess, Jr., 2003. "A Quasi-experimental Comparison of Econometric Models for Health Care Expenditures," Hunter College Department of Economics Working Papers 212, Hunter College: Department of Economics.
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    Cited by:
    1. Caterina Conigliani & Andrea Tancredi, 2009. "A Bayesian model averaging approach for cost-effectiveness analyses," Health Economics, John Wiley & Sons, Ltd., vol. 18(7), pages 807-821.
    2. Jones, A.M, 2010. "Models For Health Care," Health, Econometrics and Data Group (HEDG) Working Papers 10/01, HEDG, c/o Department of Economics, University of York.
    3. Paul C. Lambert & Lucinda J. Billingham & Nicola J. Cooper & Alex J. Sutton & Keith R. Abrams, 2008. "Estimating the cost-effectiveness of an intervention in a clinical trial when partial cost information is available: a Bayesian approach," Health Economics, John Wiley & Sons, Ltd., vol. 17(1), pages 67-81.
    4. Richard M. Nixon & David Wonderling & Richard D. Grieve, 2010. "Non-parametric methods for cost-effectiveness analysis: the central limit theorem and the bootstrap compared," Health Economics, John Wiley & Sons, Ltd., vol. 19(3), pages 316-333.
    5. Jones, A. & Lomas, J. & Rice, N., 2011. "Applying Beta-type Size Distributions to Healthcare Cost Regressions," Health, Econometrics and Data Group (HEDG) Working Papers 11/31, HEDG, c/o Department of Economics, University of York.
    6. Andrew Willan, 2011. "Sample Size Determination for Cost-Effectiveness Trials," PharmacoEconomics, Springer, vol. 29(11), pages 933-949, November.
    7. Zhao, Xiaobing & Zhou, Xian, 2012. "Estimation of medical costs by copula models with dynamic change of health status," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 480-491.
    8. Zhao, Xiaobing & Zhou, Xian, 2009. "Semiparametric modeling of medical cost data containing zeros," Statistics & Probability Letters, Elsevier, vol. 79(9), pages 1207-1214, May.
    9. Zou, Guang Yong & Taleban, Julia & Huo, Cindy Y., 2009. "Confidence interval estimation for lognormal data with application to health economics," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3755-3764, September.
    10. Anne Prenzler & Bernd Bokemeyer & J.-Matthias Schulenburg & Thomas Mittendorf, 2011. "Health care costs and their predictors of inflammatory bowel diseases in Germany," The European Journal of Health Economics, Springer, vol. 12(3), pages 273-283, June.

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