IDEAS home Printed from https://ideas.repec.org/a/spr/eujhec/v13y2012i5p549-560.html
   My bibliography  Save this article

Cost-effectiveness of open versus laparoscopic appendectomy: a multilevel approach with propensity score matching

Author

Listed:
  • Laura Haas
  • Tom Stargardt
  • Jonas Schreyoegg

Abstract

Predicted costs for LA were 1,856 US$ lower than for OA while the postoperative complication rate did not differ significantly. Thus, LA is the treatment of choice from a provider’s perspective. Copyright Springer-Verlag 2012

Suggested Citation

  • Laura Haas & Tom Stargardt & Jonas Schreyoegg, 2012. "Cost-effectiveness of open versus laparoscopic appendectomy: a multilevel approach with propensity score matching," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 13(5), pages 549-560, October.
  • Handle: RePEc:spr:eujhec:v:13:y:2012:i:5:p:549-560
    DOI: 10.1007/s10198-011-0355-6
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10198-011-0355-6
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10198-011-0355-6?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. Suthathip Yaisawarng & James F. Burgess, 2006. "Performance‐based budgeting in the public sector: an illustration from the VA health care system," Health Economics, John Wiley & Sons, Ltd., vol. 15(3), pages 295-310, March.
    2. Jonas Schreyögg, 2008. "A micro‐costing approach to estimating hospital costs for appendectomy in a Cross‐European context," Health Economics, John Wiley & Sons, Ltd., vol. 17(S1), pages 59-69, January.
    3. Richard Grieve & Richard Nixon & Simon G. Thompson & John Cairns, 2007. "Multilevel models for estimating incremental net benefits in multinational studies," Health Economics, John Wiley & Sons, Ltd., vol. 16(8), pages 815-826, August.
    4. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    5. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
    6. Garber, Alan M. & Phelps, Charles E., 1997. "Economic foundations of cost-effectiveness analysis," Journal of Health Economics, Elsevier, vol. 16(1), pages 1-31, February.
    7. Greenland, S., 1989. "Modeling and variable selection in epidemiologic analysis," American Journal of Public Health, American Public Health Association, vol. 79(3), pages 340-349.
    8. Hauck, Katharina & Street, Andrew, 2006. "Performance assessment in the context of multiple objectives: A multivariate multilevel analysis," Journal of Health Economics, Elsevier, vol. 25(6), pages 1029-1048, November.
    9. Jonas Schreyögg & Tom Stargardt & Oliver Tiemann, 2011. "Costs and quality of hospitals in different health care systems: a multi‐level approach with propensity score matching," Health Economics, John Wiley & Sons, Ltd., vol. 20(1), pages 85-100, January.
    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. Jonas Schreyögg & Tom Stargardt & Oliver Tiemann, 2011. "Costs and quality of hospitals in different health care systems: a multi‐level approach with propensity score matching," Health Economics, John Wiley & Sons, Ltd., vol. 20(1), pages 85-100, January.
    2. Timothy Tyler Brown & Juan Pablo Atal, 2019. "How robust are reference pricing studies on outpatient medical procedures? Three different preprocessing techniques applied to difference‐in differences," Health Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 280-298, February.
    3. Jeffrey Smith & Arthur Sweetman, 2016. "Viewpoint: Estimating the causal effects of policies and programs," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 871-905, August.
    4. Jung, Suhyun & Polasky, Stephen, 2018. "Partnerships to prevent deforestation in the Amazon," Journal of Environmental Economics and Management, Elsevier, vol. 92(C), pages 498-516.
    5. Michael Lechner & Anthony Strittmatter, 2019. "Practical procedures to deal with common support problems in matching estimation," Econometric Reviews, Taylor & Francis Journals, vol. 38(2), pages 193-207, February.
    6. Simone Bertoli & Francesca Marchetta, 2014. "Migration, Remittances and Poverty in Ecuador," Journal of Development Studies, Taylor & Francis Journals, vol. 50(8), pages 1067-1089, August.
    7. Oliver Tiemann & Jonas Schreyögg, 2012. "Changes in hospital efficiency after privatization," Health Care Management Science, Springer, vol. 15(4), pages 310-326, December.
    8. Patrick Christian Feihle & Jochen Lawrenz, 2017. "The Issuance of German SME Bonds and its Impact on Operating Performance," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 18(3), pages 227-259, August.
    9. Ferraro, Paul J. & Miranda, Juan José, 2014. "The performance of non-experimental designs in the evaluation of environmental programs: A design-replication study using a large-scale randomized experiment as a benchmark," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 344-365.
    10. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed," Labour Economics, Elsevier, vol. 65(C).
    11. Iacus, Stefano & Porro, Giuseppe, 2008. "Invariant and Metric Free Proximities for Data Matching: An R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i11).
    12. Kosuke Imai & Gary King & Elizabeth A. Stuart, 2008. "Misunderstandings between experimentalists and observationalists about causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 481-502, April.
    13. Kim, Hoolda & Mitra, Sophie, 2022. "The Economic and Health Effects of Long-Term Care Insurance: New Evidence from Korea," The Journal of the Economics of Ageing, Elsevier, vol. 23(C).
    14. Quỳnh Tiên Nguyên Lê & Morgan S. Polikoff, 2021. "Do English Language Development Curriculum Materials Matter for Students’ English Proficiency?," SAGE Open, , vol. 11(3), pages 21582440211, July.
    15. Boehm, Martin, 2008. "Determining the impact of internet channel use on a customer's lifetime," Journal of Interactive Marketing, Elsevier, vol. 22(3), pages 2-22.
    16. Steven Lehrer & Gregory Kordas, 2013. "Matching using semiparametric propensity scores," Empirical Economics, Springer, vol. 44(1), pages 13-45, February.
    17. Yonatan Eyal, 2020. "Self-Assessment Variables as a Source of Information in the Evaluation of Intervention Programs: A Theoretical and Methodological Framework," SAGE Open, , vol. 10(1), pages 21582440198, January.
    18. Hugo Bodory & Lorenzo Camponovo & Martin Huber & Michael Lechner, 2020. "The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 183-200, January.
    19. Seonho Shin, 2022. "Evaluating the Effect of the Matching Grant Program for Refugees: An Observational Study Using Matching, Weighting, and the Mantel-Haenszel Test," Journal of Labor Research, Springer, vol. 43(1), pages 103-133, March.
    20. Tucker, Luc, 2013. "Parliamentary Questions and the Probability of Re-election in the UK House of Commons," The Warwick Economics Research Paper Series (TWERPS) 1023, University of Warwick, Department of Economics.

    More about this item

    Keywords

    Cost-effectiveness; Laparoscopic appendectomy; Veterans health administration; Administrative data; Propensity score matching; I19; E17; C2;
    All these keywords.

    JEL classification:

    • I19 - Health, Education, and Welfare - - Health - - - Other
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

    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:spr:eujhec:v:13:y:2012:i:5:p:549-560. 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.