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Minimum Distance Matched Sampling With Fine Balance in an Observational Study of Treatment for Ovarian Cancer

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  • Rosenbaum, Paul R.
  • Ross, Richard N.
  • Silber, Jeffrey H.

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  • 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.
    • Handle: RePEc:bes:jnlasa:v:102:y:2007:p:75-83
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    Cited by:

    1. Gary King & Christopher Lucas & Richard A. Nielsen, 2017. "The Balance‐Sample Size Frontier in Matching Methods for Causal Inference," American Journal of Political Science, John Wiley & Sons, vol. 61(2), pages 473-489, April.
    2. 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.
    3. Nicholas Longford & Ioana C. Salagean, 2013. "A study of the labour market trajectories in the Grand Duchy of Luxembourg," Economics Working Papers 1396, Department of Economics and Business, Universitat Pompeu Fabra.
    4. Montalbano, Pierluigi & Nenci, Silvia & Dell'Agostino, Laura, 2022. "A non-parametric assessment of the effects of the Euro on GVC trade," International Economics, Elsevier, vol. 172(C), pages 56-76.
    5. 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.
    6. 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.
    7. Nicholas T. Longford, 2015. "Equating Without an Anchor for Nonequivalent Groups of Examinees," Journal of Educational and Behavioral Statistics, , vol. 40(3), pages 227-253, June.
    8. Kontopantelis, Evangelos, 2013. "A Greedy Algorithm for Representative Sampling: repsample in Stata," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(c01).
    9. Nicholas Longford, 2009. "A house price index defined in the potential outcomes framework," Economics Working Papers 1175, Department of Economics and Business, Universitat Pompeu Fabra.
    10. Gensler, Sonja & Leeflang, Peter & Skiera, Bernd, 2012. "Impact of online channel use on customer revenues and costs to serve: Considering product portfolios and self-selection," International Journal of Research in Marketing, Elsevier, vol. 29(2), pages 192-201.
    11. 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.
    12. 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.
    13. Shixiao Zhang & Peisong Han & Changbao Wu, 2023. "Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference," International Statistical Review, International Statistical Institute, vol. 91(2), pages 165-192, August.
    14. Florian Gunsilius & Yuliang Xu, 2021. "Matching for causal effects via multimarginal unbalanced optimal transport," Papers 2112.04398, arXiv.org, revised Jul 2022.
    15. Arpino, Bruno & Mealli, Fabrizia, 2011. "The specification of the propensity score in multilevel observational studies," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1770-1780, April.
    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. Alexander G. Nikolaev & Sheldon H. Jacobson & Wendy K. Tam Cho & Jason J. Sauppe & Edward C. Sewell, 2013. "Balance Optimization Subset Selection (BOSS): An Alternative Approach for Causal Inference with Observational Data," Operations Research, INFORMS, vol. 61(2), pages 398-412, April.
    18. 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.
    19. 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.
    20. 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.
    21. Paul R. Rosenbaum, 2011. "A New u-Statistic with Superior Design Sensitivity in Matched Observational Studies," Biometrics, The International Biometric Society, vol. 67(3), pages 1017-1027, September.
    22. 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.
    23. 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.

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