IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v108y2013i501p34-47.html
   My bibliography  Save this article

A Bayesian Procedure for File Linking to Analyze End-of-Life Medical Costs

Author

Listed:
  • Roee Gutman
  • Christopher C. Afendulis
  • Alan M. Zaslavsky

Abstract

End-of-life medical expenses are a significant proportion of all health care expenditures. These costs were studied using costs of services from Medicare claims and cause of death (CoD) from death certificates. In the absence of a unique identifier linking the two datasets, common variables identified unique matches for only 33% of deaths. The remaining cases formed cells with multiple cases (32% in cells with an equal number of cases from each file and 35% in cells with an unequal number). We sampled from the joint posterior distribution of model parameters and the permutations that link cases from the two files within each cell. The linking models included the regression of location of death on CoD and other parameters, and the regression of cost measures with a monotone missing data pattern on CoD and other demographic characteristics. Permutations were sampled by enumerating the exact distribution for small cells and by the Metropolis algorithm for large cells. Sparse matrix data structures enabled efficient calculations despite the large dataset (≈1.7 million cases). The procedure generates m datasets in which the matches between the two files are imputed. The m datasets can be analyzed independently and results can be combined using Rubin's multiple imputation rules. Our approach can be applied in other file-linking applications. Supplementary materials for this article are available online.

Suggested Citation

  • Roee Gutman & Christopher C. Afendulis & Alan M. Zaslavsky, 2013. "A Bayesian Procedure for File Linking to Analyze End-of-Life Medical Costs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 34-47, March.
  • Handle: RePEc:taf:jnlasa:v:108:y:2013:i:501:p:34-47
    DOI: 10.1080/01621459.2012.726889
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2012.726889
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/01621459.2012.726889?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.

    Citations

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


    Cited by:

    1. Betancourt, Brenda & Sosa, Juan & Rodríguez, Abel, 2022. "A prior for record linkage based on allelic partitions," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    2. John M. Abowd & Joelle Hillary Abramowitz & Margaret Catherine Levenstein & Kristin McCue & Dhiren Patki & Trivellore Raghunathan & Ann Michelle Rodgers & Matthew D. Shapiro & Nada Wasi & Dawn Zinsser, 2021. "Finding Needles in Haystacks: Multiple-Imputation Record Linkage Using Machine Learning," Working Papers 22-11, Federal Reserve Bank of Boston.
    3. Dalzell, Nicole M. & Boyd, Gale A. & Reiter, Jerome P., 2017. "Creating linked datasets for SME energy-assessment evidence-building: Results from the U.S. Industrial Assessment Center Program," Energy Policy, Elsevier, vol. 111(C), pages 95-101.
    4. Li‐Chun Zhang & Tiziana Tuoto, 2021. "Linkage‐data linear regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 522-547, April.
    5. Duncan Smith, 2020. "Re‐identification in the Absence of Common Variables for Matching," International Statistical Review, International Statistical Institute, vol. 88(2), pages 354-379, August.
    6. Nicole M. Dalzell & Jerome P. Reiter & Gale Boyd, 2017. "File Matching with Faulty Continuous Matching Variables," Working Papers 17-45, Center for Economic Studies, U.S. Census Bureau.

    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:taf:jnlasa:v:108:y:2013:i:501:p:34-47. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    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.