IDEAS home Printed from https://ideas.repec.org/a/wly/hlthec/v14y2005i8p763-776.html
   My bibliography  Save this article

The analysis of incomplete cost data due to dropout

Author

Listed:
  • Jan B. Oostenbrink
  • Maiwenn J. Al

Abstract

Incomplete data due to premature withdrawal (dropout) constitute a serious problem in prospective economic evaluations that has received only little attention to date. The aim of this simulation study was to investigate how standard methods for dealing with incomplete data perform when applied to cost data with various distributions and various types of dropout. Selected methods included the product‐limit estimator of Lin et al. the expectation maximisation (EM‐) algorithm, several types of multiple imputation (MI) and various simple methods like complete case analysis and mean imputation. Almost all methods were unbiased in the case of dropout completely at random (DCAR), but only the product‐limit estimator, the EM‐algorithm and the MI approaches provided adequate estimates of the standard error (SE). The best estimates of the mean and SE for dropout at random (DAR) were provided by the bootstrap EM‐algorithm, MI regression and MI Monte Carlo Markov chain. These methods were able to deal with skewed cost data in combination with DAR and only became biased when costs also included the costs of expensive events. None of the methods were able to deal adequately with informative dropout. In conclusion, the EM‐algorithm with bootstrap, MI regression and MI MCMC are robust to the multivariate normal assumption and are the preferred methods for the analysis of incomplete cost data when the assumption of DCAR is not justified. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Jan B. Oostenbrink & Maiwenn J. Al, 2005. "The analysis of incomplete cost data due to dropout," Health Economics, John Wiley & Sons, Ltd., vol. 14(8), pages 763-776, August.
    • Handle: RePEc:wly:hlthec:v:14:y:2005:i:8:p:763-776
    DOI: 10.1002/hec.966
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/hec.966
    Download Restriction: no
    File URL: https://libkey.io/10.1002/hec.966?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. Ghislaine van Mastrigt & Caroline van Heugten & Anne Visser-Meily & Leonarda Bremmers & Silvia Evers, 2022. "Estimating the Burden of Stroke: Two-Year Societal Costs and Generic Health-Related Quality of Life of the Restore4Stroke Cohort," IJERPH, MDPI, vol. 19(17), pages 1-15, September.
    2. M. Carreras & M. García-Goñi & P. Ibern & J. Coderch & L. Vall-Llosera & J. Inoriza, 2011. "Estimates of patient costs related with population morbidity: can indirect costs affect the results?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 12(4), pages 289-295, August.
    3. Bernhard Michalowsky & Wolfgang Hoffmann & Kevin Kennedy & Feng Xie, 2020. "Is the whole larger than the sum of its parts? Impact of missing data imputation in economic evaluation conducted alongside randomized controlled trials," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(5), pages 717-728, July.
    4. Fatma Karapinar-Çarkıt & Ronald van der Knaap & Fatiha Bouhannouch & Sander D Borgsteede & Marjo J A Janssen & Carl E H Siegert & Toine C G Egberts & Patricia M L A van den Bemt & Marieke F van Wier &, 2017. "Cost-effectiveness of a transitional pharmaceutical care program for patients discharged from the hospital," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-20, April.
    5. Mohamed El Alili & Johanna M. Dongen & Judith A. F. Huirne & Maurits W. Tulder & Judith E. Bosmans, 2017. "Reporting and Analysis of Trial-Based Cost-Effectiveness Evaluations in Obstetrics and Gynaecology," PharmacoEconomics, Springer, vol. 35(10), pages 1007-1033, October.
    6. Judith Bosmans & Oscar Brook & Hein Hout & Martine Bruijne & Hugo Nieuwenhuyse & Lex Bouter & Wim Stalman & Maurits Tulder, 2007. "Cost Effectiveness of a Pharmacy-Based Coaching Programme to Improve Adherence to Antidepressants," PharmacoEconomics, Springer, vol. 25(1), pages 25-37, January.
    7. M. Carreras & Manuel García-Goñi & Pere Ibern & J. Coderch & L. Vall-Llosera & José María Inoriza, 2009. "Estimates of patient costs related with population morbidity: Can indirect costs affect the results?," Economics Working Papers 1138, Department of Economics and Business, Universitat Pompeu Fabra.
    8. Manuel Gomes & Karla Díaz-Ordaz & Richard Grieve & Michael G. Kenward, 2013. "Multiple Imputation Methods for Handling Missing Data in Cost-effectiveness Analyses That Use Data from Hierarchical Studies," Medical Decision Making, , vol. 33(8), pages 1051-1063, November.
    9. Rita Faria & Manuel Gomes & David Epstein & Ian White, 2014. "A Guide to Handling Missing Data in Cost-Effectiveness Analysis Conducted Within Randomised Controlled Trials," PharmacoEconomics, Springer, vol. 32(12), pages 1157-1170, December.
    10. Marike R. C. Hendriks & Maiwenn J. Al & Michel H. C. Bleijlevens & Jolanda C. M. van Haastregt & Harry F. J. M. Crebolder & Jacques Th. M. van Eijk & Silvia M. A. A. Evers, 2013. "Continuous versus Intermittent Data Collection of Health Care Utilization," Medical Decision Making, , vol. 33(8), pages 998-1008, November.
    11. Baptiste Leurent & Manuel Gomes & Suzie Cro & Nicola Wiles & James R. Carpenter, 2020. "Reference‐based multiple imputation for missing data sensitivity analyses in trial‐based cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 171-184, February.
    12. Andrea Gabrio & Alexina J. Mason & Gianluca Baio, 2017. "Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations," PharmacoEconomics - Open, Springer, vol. 1(2), pages 79-97, June.
    13. Mohamed El Alili & Johanna M. van Dongen & Jonas L. Esser & Martijn W. Heymans & Maurits W. van Tulder & Judith E. Bosmans, 2022. "A scoping review of statistical methods for trial‐based economic evaluations: The current state of play," Health Economics, John Wiley & Sons, Ltd., vol. 31(12), pages 2680-2699, December.
    14. David K. Blough & Scott Ramsey & Sean D. Sullivan & Roger Yusen, 2009. "The impact of using different imputation methods for missing quality of life scores on the estimation of the cost‐effectiveness of lung‐volume‐reduction surgery," Health Economics, John Wiley & Sons, Ltd., vol. 18(1), pages 91-101, January.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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. 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.
    9. Bernadette Li & John Cairns & James Fotheringham & Rommel Ravanan, 2016. "Predicting hospital costs for patients receiving renal replacement therapy to inform an economic evaluation," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(6), pages 659-668, July.
    10. Manca, A & Austin, P. C, 2008. "Using propensity score methods to analyse individual patient-level cost-effectiveness data from observational studies," Health, Econometrics and Data Group (HEDG) Working Papers 08/20, HEDG, c/o Department of Economics, University of York.
    11. Mohamed El Alili & Johanna M. van Dongen & Jonas L. Esser & Martijn W. Heymans & Maurits W. van Tulder & Judith E. Bosmans, 2022. "A scoping review of statistical methods for trial‐based economic evaluations: The current state of play," Health Economics, John Wiley & Sons, Ltd., vol. 31(12), pages 2680-2699, December.
    12. Zhao, Xiaobing & Zhou, Xian, 2009. "Semiparametric modeling of medical cost data containing zeros," Statistics & Probability Letters, Elsevier, vol. 79(9), pages 1207-1214, May.

    More about this item

    Statistics

    Access and download statistics

    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:wly:hlthec:v:14:y:2005:i:8:p:763-776. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/5749 .

    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.