IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v79y2023i4p3981-3997.html
   My bibliography  Save this article

Efficient algorithms for building representative matched pairs with enhanced generalizability

Author

Listed:
  • Bo Zhang

Abstract

Many recent efforts center on assessing the ability of real‐world evidence (RWE) generated from non‐randomized, observational data to produce results compatible with those from randomized controlled trials (RCTs). One noticeable endeavor is the RCT DUPLICATE initiative. To better reconcile findings from an observational study and an RCT, or two observational studies based on different databases, it is desirable to eliminate differences between study populations. We outline an efficient, network‐flow‐based statistical matching algorithm that designs well‐matched pairs from observational data that resemble the covariate distributions of a target population, for instance, the target‐RCT‐eligible population in the RCT DUPLICATE initiative studies or a generic population of scientific interest. We demonstrate the usefulness of the method by revisiting the inconsistency regarding a cardioprotective effect of the hormone replacement therapy (HRT) in the Women's Health Initiative (WHI) clinical trial and corresponding observational study. We found that the discrepancy between the trial and observational study persisted in a design that adjusted for the difference in study populations' cardiovascular risk profile, but seemed to disappear in a study design that further adjusted for the difference in HRT initiation age and previous estrogen‐plus‐progestin use. The proposed method is integrated into the R package match2C.

Suggested Citation

  • Bo Zhang, 2023. "Efficient algorithms for building representative matched pairs with enhanced generalizability," Biometrics, The International Biometric Society, vol. 79(4), pages 3981-3997, December.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:3981-3997
    DOI: 10.1111/biom.13919
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13919
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13919?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. Hao Chen & Dylan S. Small, 2022. "New multivariate tests for assessing covariate balance in matched observational studies," Biometrics, The International Biometric Society, vol. 78(1), pages 202-213, March.
    2. Giovanni Nattino & Bo Lu & Junxin Shi & Stanley Lemeshow & Henry Xiang, 2021. "Triplet Matching for Estimating Causal Effects With Three Treatment Arms: A Comparative Study of Mortality by Trauma Center Level," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(533), pages 44-53, March.
    3. Issa J. Dahabreh & Sarah E. Robertson & Eric J. Tchetgen & Elizabeth A. Stuart & Miguel A. Hernán, 2019. "Generalizing causal inferences from individuals in randomized trials to all trial‐eligible individuals," Biometrics, The International Biometric Society, vol. 75(2), pages 685-694, June.
    4. Ruoqi Yu, 2021. "Evaluating and improving a matched comparison of antidepressants and bone density," Biometrics, The International Biometric Society, vol. 77(4), pages 1276-1288, December.
    5. Rosenbaum, Paul R., 2010. "Design Sensitivity and Efficiency in Observational Studies," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 692-702.
    6. 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.
    7. Elizabeth A. Stuart & Stephen R. Cole & Catherine P. Bradshaw & Philip J. Leaf, 2011. "The use of propensity scores to assess the generalizability of results from randomized trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 369-386, April.
    8. José R. Zubizarreta, 2012. "Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1360-1371, December.
    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. Jason J. Sauppe & Sheldon H. Jacobson, 2017. "The role of covariate balance in observational studies," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(4), pages 323-344, June.
    2. Siyu Heng & Hyunseung Kang & Dylan S. Small & Colin B. Fogarty, 2021. "Increasing power for observational studies of aberrant response: An adaptive approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 482-504, July.
    3. Reed, Deborah K. & Aloe, Ariel M., 2020. "Interpreting the effectiveness of a summer reading program: The eye of the beholder," Evaluation and Program Planning, Elsevier, vol. 83(C).
    4. España, F. & Arriagada, R. & Melo, O. & Foster, W., 2022. "Forest plantation subsidies: Impact evaluation of the Chilean case," Forest Policy and Economics, Elsevier, vol. 137(C).
    5. Yves Tillé, 2022. "Some Solutions Inspired by Survey Sampling Theory to Build Effective Clinical Trials," International Statistical Review, International Statistical Institute, vol. 90(3), pages 481-498, December.
    6. Glazer Amanda K. & Pimentel Samuel D., 2023. "Robust inference for matching under rolling enrollment," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-19, January.
    7. Fukui Hideki, 2023. "Evaluating Different Covariate Balancing Methods: A Monte Carlo Simulation," Statistics, Politics and Policy, De Gruyter, vol. 14(2), pages 205-326, June.
    8. Zhexiao Lin & Peng Ding & Fang Han, 2023. "Estimation Based on Nearest Neighbor Matching: From Density Ratio to Average Treatment Effect," Econometrica, Econometric Society, vol. 91(6), pages 2187-2217, November.
    9. Bikram Karmakar, 2022. "An approximation algorithm for blocking of an experimental design," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1726-1750, November.
    10. Huang, Wei & Li, Fan & Liao, Xiaowei & Hu, Pingping, 2018. "More money, better performance? The effects of student loans and need-based grants in China's higher education," China Economic Review, Elsevier, vol. 51(C), pages 208-227.
    11. Franz R. Hahn & Werner Hölzl & Claudia Kwapil, 2016. "The Credit Channel and the Role of Monetary Policy Before, During and After the Global Financial Crisis. A Micro Data Approach to the Analysis of Bank-firm Relationships," WIFO Studies, WIFO, number 59233.
    12. Elizabeth Tipton, 2014. "How Generalizable Is Your Experiment? An Index for Comparing Experimental Samples and Populations," Journal of Educational and Behavioral Statistics, , vol. 39(6), pages 478-501, December.
    13. Cousineau, Martin & Verter, Vedat & Murphy, Susan A. & Pineau, Joelle, 2023. "Estimating causal effects with optimization-based methods: A review and empirical comparison," European Journal of Operational Research, Elsevier, vol. 304(2), pages 367-380.
    14. Jason J. Sauppe & Sheldon H. Jacobson & Edward C. Sewell, 2014. "Complexity and Approximation Results for the Balance Optimization Subset Selection Model for Causal Inference in Observational Studies," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 547-566, August.
    15. Martin Cousineau & Vedat Verter & Susan A. Murphy & Joelle Pineau, 2022. "Estimating causal effects with optimization-based methods: A review and empirical comparison," Papers 2203.00097, arXiv.org.
    16. Ruoqi Yu, 2023. "How well can fine balance work for covariate balancing," Biometrics, The International Biometric Society, vol. 79(3), pages 2346-2356, September.
    17. Stefan KIRCHWEGER & Jochen KANTELHARDT & Friedrich LEISCH, 2015. "Impacts of the government-supported investments on the economic farm performance in Austria," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 61(8), pages 343-355.
    18. Bo Zhang & Dylan S. Small, 2020. "A calibrated sensitivity analysis for matched observational studies with application to the effect of second‐hand smoke exposure on blood lead levels in children," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1285-1305, November.
    19. Ruoqi Yu, 2021. "Evaluating and improving a matched comparison of antidepressants and bone density," Biometrics, The International Biometric Society, vol. 77(4), pages 1276-1288, December.
    20. Stephen C. Nelson & Geoffrey P. R. Wallace, 2017. "Are IMF lending programs good or bad for democracy?," The Review of International Organizations, Springer, vol. 12(4), pages 523-558, December.

    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:bla:biomet:v:79:y:2023:i:4:p:3981-3997. 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://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.