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Predicting kidney transplant outcomes with partial knowledge of HLA mismatch

Author

Listed:
  • Charles F. Manski

    (Department of Economics, Northwestern University, Evanston, IL 60208; Institute for Policy Research, Northwestern University, Evanston, IL 60208)

  • Anat R. Tambur

    (Feinberg School of Medicine, Northwestern University, Chicago, IL 60611)

  • Michael Gmeiner

    (Department of Economics, Northwestern University, Evanston, IL 60208)

Abstract

We consider prediction of graft survival when a kidney from a deceased donor is transplanted into a recipient, with a focus on the variation of survival with degree of human leukocyte antigen (HLA) mismatch. Previous studies have used data from the Scientific Registry of Transplant Recipients (SRTR) to predict survival conditional on partial characterization of HLA mismatch. Whereas earlier studies assumed proportional hazards models, we used nonparametric regression methods. These do not make the unrealistic assumption that relative risks are invariant as a function of time since transplant, and hence should be more accurate. To refine the predictions possible with partial knowledge of HLA mismatch, it has been suggested that HaploStats statistics on the frequencies of haplotypes within specified ethnic/national populations be used to impute complete HLA types. We counsel against this, showing that it cannot improve predictions on average and sometimes yields suboptimal transplant decisions. We show that the HaploStats frequency statistics are nevertheless useful when combined appropriately with the SRTR data. Analysis of the ecological inference problem shows that informative bounds on graft survival probabilities conditional on refined HLA typing are achievable by combining SRTR and HaploStats data with immunological knowledge of the relative effects of mismatch at different HLA loci.

Suggested Citation

  • Charles F. Manski & Anat R. Tambur & Michael Gmeiner, 2019. "Predicting kidney transplant outcomes with partial knowledge of HLA mismatch," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(41), pages 20339-20345, October.
  • Handle: RePEc:nas:journl:v:116:y:2019:p:20339-20345
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    Citations

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    Cited by:

    1. Sheyu Li & Valentyn Litvin & Charles F. Manski, 2022. "Partial Identification of Personalized Treatment Response with Trial-reported Analyses of Binary Subgroups," NBER Working Papers 30461, National Bureau of Economic Research, Inc.
    2. Itai Ashlagi & Alvin E. Roth, 2021. "Kidney Exchange: An Operations Perspective," Management Science, INFORMS, vol. 67(9), pages 5455-5478, September.
    3. Charles F. Manski, 2022. "Inference with Imputed Data: The Allure of Making Stuff Up," Papers 2205.07388, arXiv.org.
    4. Charles F Manski & Michael Gmeiner & Anat Tamburc, 2021. "Misguided Use of Observed Covariates to Impute Missing Covariates in Conditional Prediction: A Shrinkage Problem," Papers 2102.11334, arXiv.org.

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