IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v28y2019i1d10.1007_s10260-018-0428-0.html
   My bibliography  Save this article

Modeling right-censored medical cost data in regression and the effects of covariates

Author

Listed:
  • Lu Deng

    (Central University of Finance and Economics)

  • Wendy Lou

    (University of Toronto)

  • Nicholas Mitsakakis

    (University Health Network
    University of Toronto)

Abstract

This paper focuses on the problem of modeling medical costs with covariates when the cost data are subject to right-censoring. The prevailing methods are divided into three categories, (a) the inverse probability weighted (IPW) regressions; (b) the generalized survival-adjusted estimators; and (c) the joint-modeling methods. Comparisons are made both in and between categories to demonstrate their different mechanisms to handle the informative censoring, to take into account the covariates and the way they interpret the covariates effects. Based on the above discussion, we believe that the linear or generalized linear regressions using the IPW scheme are very popular due to its convenience to fit and interpret, which could be a good choice in practice with additional conditional means to address the role of survival to some extent. The recently proposed generalized survival-adjusted estimator is very intuitive as the derivative of the estimation function naturally decomposes the effects of covariates into the intensity part and the survival part, therefore especially useful when the covariates have substantial effect on survival. The joint-modelling methods have the advantage in providing the access to the correlation between medical cost and survival, although they suffer from theoretical and computational complexity. The effect of covariates on cost through survival in this kind of joint-modelling methods could be a desirable topic for further research.

Suggested Citation

  • Lu Deng & Wendy Lou & Nicholas Mitsakakis, 2019. "Modeling right-censored medical cost data in regression and the effects of covariates," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(1), pages 143-155, March.
    • Handle: RePEc:spr:stmapp:v:28:y:2019:i:1:d:10.1007_s10260-018-0428-0
    DOI: 10.1007/s10260-018-0428-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-018-0428-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10260-018-0428-0?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. Hongwei Zhao & Chen Zuo & Shuai Chen & Heejung Bang, 2012. "Nonparametric Inference for Median Costs with Censored Data," Biometrics, The International Biometric Society, vol. 68(3), pages 717-725, September.
    2. Onur Başer & Joseph C. Gardiner & Cathy J. Bradley & Hüseyin Yüce & Charles Given, 2006. "Longitudinal analysis of censored medical cost data," Health Economics, John Wiley & Sons, Ltd., vol. 15(5), pages 513-525, May.
    3. Liu, Lei & Conaway, Mark R. & Knaus, William A. & Bergin, James D., 2008. "A random effects four-part model, with application to correlated medical costs," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4458-4473, May.
    4. Anirban Basu & Willard G. Manning, 2010. "Estimating lifetime or episode‐of‐illness costs under censoring," Health Economics, John Wiley & Sons, Ltd., vol. 19(9), pages 1010-1028, September.
    5. Etzioni, Ruth D. & Feuer, Eric J. & Sullivan, Sean D. & Lin, Danyu & Hu, Chengcheng & Ramsey, Scott D., 1999. "On the use of survival analysis techniques to estimate medical care costs," Journal of Health Economics, Elsevier, vol. 18(3), pages 365-380, June.
    6. Heejung Bang & Anastasios A. Tsiatis, 2002. "Median Regression with Censored Cost Data," Biometrics, The International Biometric Society, vol. 58(3), pages 643-649, September.
    7. O'Hagan, Anthony & Stevens, John W., 2004. "On estimators of medical costs with censored data," Journal of Health Economics, Elsevier, vol. 23(3), pages 615-625, May.
    8. Huang Y., 2002. "Calibration Regression of Censored Lifetime Medical Cost," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 318-327, March.
    9. Raikou, M. & McGuire, A., 2004. "Estimating medical care costs under conditions of censoring," Journal of Health Economics, Elsevier, vol. 23(3), pages 443-470, May.
    10. Quesenberry Jr., C.P. & Fireman, B. & Hiatt, R.A. & Selby, J.V., 1989. "A survival analysis of hospitalization among patients with acquired immunodeficiency syndrome," American Journal of Public Health, American Public Health Association, vol. 79(12), pages 1643-1647.
    11. Hongwei Zhao & Lili Tian, 2001. "On Estimating Medical Cost and Incremental Cost-Effectiveness Ratios with Censored Data," Biometrics, The International Biometric Society, vol. 57(4), pages 1002-1008, December.
    12. Baser, Onur & Gardiner, Joseph C & Bradley, Cathy J & Given, Charles W, 2004. "Estimation from Censored Medical Cost Data," MPRA Paper 102198, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    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. Liu, Lei & Conaway, Mark R. & Knaus, William A. & Bergin, James D., 2008. "A random effects four-part model, with application to correlated medical costs," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4458-4473, May.
    2. Y. T. Hwang & C. H. Huang & W. L. Yeh & Y. D. Shen, 2017. "The weighted general linear model for longitudinal medical cost data – an application in colorectal cancer," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(2), pages 288-307, January.
    3. Maria Raikou & Alistair McGuire, 2012. "Estimating Costs for Economic Evaluation," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 43, Edward Elgar Publishing.
    4. Zhao, Xiaobing & Zhou, Xian, 2009. "Semiparametric modeling of medical cost data containing zeros," Statistics & Probability Letters, Elsevier, vol. 79(9), pages 1207-1214, May.
    5. Basu A & Manning WG, 2009. "Estimating Lifetime or Episode-of-illness Costs," Health, Econometrics and Data Group (HEDG) Working Papers 09/12, HEDG, c/o Department of Economics, University of York.
    6. Hongwei Zhao & Chen Zuo & Shuai Chen & Heejung Bang, 2012. "Nonparametric Inference for Median Costs with Censored Data," Biometrics, The International Biometric Society, vol. 68(3), pages 717-725, September.
    7. Jing‐Shiang Hwang & Tsuey‐Hwa Hu & Lukas Jyuhn‐Hsiarn Lee & Jung‐Der Wang, 2017. "Estimating lifetime medical costs from censored claims data," Health Economics, John Wiley & Sons, Ltd., vol. 26(12), pages 332-344, December.
    8. Anirban Basu & Willard G. Manning, 2010. "Estimating lifetime or episode‐of‐illness costs under censoring," Health Economics, John Wiley & Sons, Ltd., vol. 19(9), pages 1010-1028, September.
    9. Raikou, Maria & McGuire, Alistair, 2009. "Parametric estimation of medical care costs under conditions of censoring," LSE Research Online Documents on Economics 28857, London School of Economics and Political Science, LSE Library.
    10. Jie Zhou & Xin Chen & Xinyuan Song & Liuquan Sun, 2021. "A joint modeling approach for analyzing marker data in the presence of a terminal event," Biometrics, The International Biometric Society, vol. 77(1), pages 150-161, March.
    11. 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.
    12. Xie, Shangyu & Wan, Alan T.K. & Zhou, Yong, 2015. "Quantile regression methods with varying-coefficient models for censored data," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 154-172.
    13. L. Tian & J. Liu & Y. Zhao & L. J. Wei, 2004. "Statistical inference based on non-smooth estimating functions," Biometrika, Biometrika Trust, vol. 91(4), pages 943-954, December.
    14. De Backer, Mickael & El Ghouch, Anouar & Van Keilegom, Ingrid, 2017. "An Adapted Loss Function for Censored Quantile Regression," LIDAM Discussion Papers ISBA 2017003, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    15. Tang, Linjun & Zhou, Zhangong & Wu, Changchun, 2012. "Weighted composite quantile estimation and variable selection method for censored regression model," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 653-663.
    16. Jihnhee Yu & Albert Vexler & Lili Tian, 2010. "Analyzing Incomplete Data Subject to a Threshold using Empirical Likelihood Methods: An Application to a Pneumonia Risk Study in an ICU Setting," Biometrics, The International Biometric Society, vol. 66(1), pages 123-130, March.
    17. Lei Liu & Xuelin Huang & John O'Quigley, 2008. "Analysis of Longitudinal Data in the Presence of Informative Observational Times and a Dependent Terminal Event, with Application to Medical Cost Data," Biometrics, The International Biometric Society, vol. 64(3), pages 950-958, September.
    18. Kyu Hyun Kim & Daniel J. Caplan & Sangwook Kang, 2023. "Smoothed quantile regression for censored residual life," Computational Statistics, Springer, vol. 38(2), pages 1001-1022, June.
    19. Sungwan Bang & Soo-Heang Eo & Yong Mee Cho & Myoungshic Jhun & HyungJun Cho, 2016. "Non-crossing weighted kernel quantile regression with right censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 100-121, January.
    20. Cao, Yongxiu & Yu, Jichang, 2023. "Adjusting for unmeasured confounding in survival causal effect using validation data," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).

    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:stmapp:v:28:y:2019:i:1:d:10.1007_s10260-018-0428-0. 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 .

    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.