IDEAS home Printed from https://ideas.repec.org/a/spr/hecrev/v5y2015i1p1-1610.1186-s13561-015-0045-7.html
   My bibliography  Save this article

Statistical models for the analysis of skewed healthcare cost data: a simulation study

Author

Listed:
  • Amal Malehi
  • Fatemeh Pourmotahari
  • Kambiz Angali

Abstract

Skewed data is the main issue in statistical models in healthcare costs. Data transformation is a conventional method to decrease skewness, but there are some disadvantages. Some recent studies have employed generalized linear models (GLMs) and Cox proportional hazard regression as alternative estimators. The aim of this study was to investigate how well these alternative estimators perform in terms of bias and precision when the data are skewed. The primary outcome was an estimation of population means of healthcare costs and the secondary outcome was the impact of a covariate on healthcare cost. Alternative estimators, such as ordinary least squares (OLS) for Ln(y) or Log(y), Gamma, Weibull and Cox proportional hazard regression models, were compared using Monte Carlo simulation under different situations, which were generated from skewed distributions. We found that there was not one best model across all generated conditions. However, GLMs, especially the Gamma regression model, behaved well in the estimation of population means of healthcare costs. The results showed that the Cox proportional hazard model exhibited a poor estimation of population means of healthcare costs and the β 1 even under proportional hazard data. Approximately results are consistent by increasing the sample size. However, increasing the sample size could improve the performance of the OLS-based model. Copyright Malehi et al.; licensee Springer. 2015

Suggested Citation

  • Amal Malehi & Fatemeh Pourmotahari & Kambiz Angali, 2015. "Statistical models for the analysis of skewed healthcare cost data: a simulation study," Health Economics Review, Springer, vol. 5(1), pages 1-16, December.
    • Handle: RePEc:spr:hecrev:v:5:y:2015:i:1:p:1-16:10.1186/s13561-015-0045-7
    DOI: 10.1186/s13561-015-0045-7
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1186/s13561-015-0045-7
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1186/s13561-015-0045-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Anirban Basu & Willard G. Manning & John Mullahy, 2004. "Comparing alternative models: log vs Cox proportional hazard?," Health Economics, John Wiley & Sons, Ltd., vol. 13(8), pages 749-765, August.
    2. Partha Deb & James F. Burgess, Jr., 2003. "A Quasi-experimental Comparison of Econometric Models for Health Care Expenditures," Economics Working Paper Archive at Hunter College 212, Hunter College Department of Economics.
    3. Manning, Willard G. & Mullahy, John, 2001. "Estimating log models: to transform or not to transform?," Journal of Health Economics, Elsevier, vol. 20(4), pages 461-494, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nam-Hee Kim & Se-Hwan Jung & Ichiro Kawachi, 2021. "Did Expanded Dental Insurance Reduce Out-of-Pocket Expenditures on Dental Care among Older Adults in Korea? Interrupted Time-Series Analysis," IJERPH, MDPI, vol. 18(6), pages 1-9, March.
    2. Nisha Nataraj & Julie Simmons Ivy & Fay Cobb Payton & Joseph Norman, 2018. "Diabetes and the hospitalized patient," Health Care Management Science, Springer, vol. 21(4), pages 534-553, December.
    3. Stephen Rocks & Daniela Berntson & Alejandro Gil-Salmerón & Mudathira Kadu & Nieves Ehrenberg & Viktoria Stein & Apostolos Tsiachristas, 2020. "Cost and effects of integrated care: a systematic literature review and meta-analysis," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(8), pages 1211-1221, November.
    4. A. Mukasheva & N. Saparkhojayev & Z. Akanov & A. Algazieva, 2019. "The Prevalence of Diabetes in the Republic of Kazakhstan Based on Regression Analysis Methods," International Journal of Health and Medical Sciences, Mohammad A. H. Khan, vol. 5(1), pages 8-16.
    5. Seung Ju Kim & Sun Jung Kim & Kyu-Tae Han & Eun-Cheol Park, 2017. "Medical costs, Cesarean delivery rates, and length of stay in specialty hospitals vs. non-specialty hospitals in South Korea," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-11, November.
    6. Miszczyńska Katarzyna M. & Miszczyński Piotr M., 2020. "Inpatient Costs in the Perspective of Polish Health Policy: Scenario Analysis," South East European Journal of Economics and Business, Sciendo, vol. 15(2), pages 43-56, December.
    7. Yizhe Xu & Tom H. Greene & Adam P. Bress & Brandon K. Bellows & Yue Zhang & Zugui Zhang & Paul Kolm & William S. Weintraub & Andrew S. Moran & Jincheng Shen, 2022. "An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random Forest," Papers 2204.10971, arXiv.org.
    8. Jiwoo Lee & Sakari Jukarainen & Antti Karvanen & Padraig Dixon & Neil M. Davies & George Davey Smith & Pradeep Natarajan & Andrea Ganna, 2023. "Quantifying the causal impact of biological risk factors on healthcare costs," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Borislava Mihaylova & Andrew Briggs & Anthony O'Hagan & Simon G. Thompson, 2011. "Review of statistical methods for analysing healthcare resources and costs," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 897-916, August.
    3. 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.
    4. 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.
    5. Thompson, Simon G. & Nixon, Richard M. & Grieve, Richard, 2006. "Addressing the issues that arise in analysing multicentre cost data, with application to a multinational study," Journal of Health Economics, Elsevier, vol. 25(6), pages 1015-1028, November.
    6. Law, Michael R. & Grépin, Karen A., 2010. "Is newer always better? Re-evaluating the benefits of newer pharmaceuticals," Journal of Health Economics, Elsevier, vol. 29(5), pages 743-750, September.
    7. John Mullahy, 2015. "In Memoriam: Willard G. Manning, 1946‐2014," Health Economics, John Wiley & Sons, Ltd., vol. 24(3), pages 253-257, March.
    8. Andrew Briggs & Richard Nixon & Simon Dixon & Simon Thompson, 2005. "Parametric modelling of cost data: some simulation evidence," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 421-428, April.
    9. Stargardt, Tom & Schreyögg, Jonas, 2012. "A framework to evaluate the effects of small area variations in healthcare infrastructure on diagnostics and patient outcomes of rare diseases based on administrative data," Health Policy, Elsevier, vol. 105(2), pages 110-118.
    10. Manos Matsaganis & Theodore Mitrakos & Panos Tsakloglou, 2008. "Modelling Household Expenditure on Health Care in Greece," Working Papers 68, Bank of Greece.
    11. Besstremyannaya, Galina, 2017. "Measuring income equity in the demand for healthcare with finite mixture models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 46, pages 5-29.
    12. Manos Matsaganis & Theodore Mitrakos & Panos Tsakloglou, 2009. "Modelling health expenditure at the household level in Greece," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 10(3), pages 329-336, July.
    13. 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.
    14. 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.
    15. Anirban Basu & Bhakti V. Arondekar & Paul J. Rathouz, 2006. "Scale of interest versus scale of estimation: comparing alternative estimators for the incremental costs of a comorbidity," Health Economics, John Wiley & Sons, Ltd., vol. 15(10), pages 1091-1107, October.
    16. Richard M. Nixon & Simon G. Thompson, 2005. "Methods for incorporating covariate adjustment, subgroup analysis and between‐centre differences into cost‐effectiveness evaluations," Health Economics, John Wiley & Sons, Ltd., vol. 14(12), pages 1217-1229, December.
    17. Steven C. Hill & G. Edward Miller, 2010. "Health expenditure estimation and functional form: applications of the generalized gamma and extended estimating equations models," Health Economics, John Wiley & Sons, Ltd., vol. 19(5), pages 608-627, May.
    18. 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.
    19. 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.
    20. Ileana Baldi & Eva Pagano & Paola Berchialla & Alessandro Desideri & Alberto Ferrando & Franco Merletti & Dario Gregori, 2013. "Modeling healthcare costs in simultaneous presence of asymmetry, heteroscedasticity and correlation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(2), pages 298-310, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:hecrev:v:5:y:2015:i:1:p:1-16:10.1186/s13561-015-0045-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com/economics/journal/13561 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.