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Optimal Matching with Minimal Deviation from Fine Balance in a Study of Obesity and Surgical Outcomes

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  • Dan Yang
  • Dylan S. Small
  • Jeffrey H. Silber
  • Paul R. Rosenbaum

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  • Dan Yang & Dylan S. Small & Jeffrey H. Silber & Paul R. Rosenbaum, 2012. "Optimal Matching with Minimal Deviation from Fine Balance in a Study of Obesity and Surgical Outcomes," Biometrics, The International Biometric Society, vol. 68(2), pages 628-636, June.
    • Handle: RePEc:bla:biomet:v:68:y:2012:i:2:p:628-636
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2011.01691.x
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    References listed on IDEAS

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    1. Lu, Bo & Greevy, Robert & Xu, Xinyi & Beck, Cole, 2011. "Optimal Nonbipartite Matching and Its Statistical Applications," The American Statistician, American Statistical Association, vol. 65(1), pages 21-30.
    2. Tan, Zhiqiang, 2006. "A Distributional Approach for Causal Inference Using Propensity Scores," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1619-1637, December.
    3. Heller, Ruth & Rosenbaum, Paul R. & Small, Dylan S., 2010. "Using the Cross-Match Test to Appraise Covariate Balance in Matched Pairs," The American Statistician, American Statistical Association, vol. 64(4), pages 299-309.
    4. Ben B. Hansen, 2008. "The prognostic analogue of the propensity score," Biometrika, Biometrika Trust, vol. 95(2), pages 481-488.
    5. Rosenbaum, Paul R. & Ross, Richard N. & Silber, Jeffrey H., 2007. "Minimum Distance Matched Sampling With Fine Balance in an Observational Study of Treatment for Ovarian Cancer," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 75-83, March.
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    Cited by:

    1. Samuel D. Pimentel & Lauren Vollmer Forrow & Jonathan Gellar & Jiaqi Li, 2020. "Optimal matching approaches in health policy evaluations under rolling enrolment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1411-1435, October.
    2. 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.
    3. 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.
    4. Tian Heong Chan & Francis de Véricourt & Omar Besbes, 2019. "Contracting in Medical Equipment Maintenance Services: An Empirical Investigation," Management Science, INFORMS, vol. 65(3), pages 1136-1150, March.
    5. 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.
    6. Luke Keele & Steve Harris & Samuel D. Pimentel & Richard Grieve, 2020. "Stronger instruments and refined covariate balance in an observational study of the effectiveness of prompt admission to intensive care units," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1501-1521, October.
    7. 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.
    8. Ruoqi Yu, 2023. "How well can fine balance work for covariate balancing," Biometrics, The International Biometric Society, vol. 79(3), pages 2346-2356, September.
    9. Hochbaum, Dorit S. & Rao, Xu & Sauppe, Jason, 2022. "Network flow methods for the minimum covariate imbalance problem," European Journal of Operational Research, Elsevier, vol. 300(3), pages 827-836.

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