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Statistics in Biosciences

, Volume 6, Issue 1, pp 85–104 | Cite as

Optimal Decisions for Organ Exchanges in a Kidney Paired Donation Program

  • Yijiang Li
  • Peter X.-K. Song
  • Yan Zhou
  • Alan B. Leichtman
  • Michael A. Rees
  • John D. Kalbfleisch
Article

Abstract

The traditional concept of barter exchange in economics has been extended in the modern era to the area of living-donor kidney transplantation, where one incompatible donor-candidate pair is matched to another pair with a complementary incompatibility, such that the donor from one pair gives an organ to a compatible candidate in the other pair and vice versa. Kidney paired donation (KPD) programs provide a unique and important platform for living incompatible donor-candidate pairs to exchange organs in order to achieve mutual benefit. In this paper, we propose novel organ allocation strategies to arrange kidney exchanges under uncertainties with advantages, including (i) allowance for a general utility-based evaluation of potential kidney transplants and an explicit consideration of stochastic features inherent in a KPD program; and (ii) exploitation of possible alternative exchanges when the originally planned allocation cannot be fully executed. This allocation strategy is implemented using an integer programming (IP) formulation, and its implication is assessed via a data-based simulation system by tracking an evolving KPD program over a series of match runs. Extensive simulation studies are provided to illustrate our proposed approach.

Keywords

Decision under uncertainty Expected utility Integer programming Probabilistic modeling Organ exchanges 

Notes

Acknowledgements

The authors thank the support from the Michigan Institute for Clinical and Health Research (MICHR), Michigan School of Public Health, Scientific Registry of Transplant Recipients, and National Institute of General Medical Sciences (NIGMS). The authors are also grateful for invaluable suggestions and comments from Drs. Yanhua Chen, David Abraham and Tuomas Sandholm. The authors would like to thank an anonymous referee whose comments helped to improve this manuscript. This work was supported in part by a grant from the National Institutes of Health (NIH) CTSA at the University of Michigan 2UL1TR000433-06 and by the NIH grant 1R01-DK093513.

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Copyright information

© International Chinese Statistical Association 2013

Authors and Affiliations

  • Yijiang Li
    • 1
  • Peter X.-K. Song
    • 2
  • Yan Zhou
    • 2
  • Alan B. Leichtman
    • 3
  • Michael A. Rees
    • 4
  • John D. Kalbfleisch
    • 2
  1. 1.Google Inc.Mountain ViewUSA
  2. 2.Department of BiostatisticsUniversity of MichiganAnn ArborUSA
  3. 3.Department of Internal MedicineUniversity of MichiganAnn ArborUSA
  4. 4.Department of UrologyUniversity of Toledo Medical CenterToledoUSA

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