Optimal donor selection with multiple outcomes for hematopoietic stem cell transplant recipients by Bayesian nonparametric machine learning
Communications in Statistics Case Studies Data Analysis and Applications, pp. 1–22
Abstract
Allogeneic hematopoietic stem cell transplantation is among the few treatment options for patients suffering from life-threatening hematologic malignancies; yet, the possible adverse complications can be serious and even fatal. Matching an unrelated donor with a recipient by human leukocyte antigens (HLA) is widely accepted. However, there is lesser agreement about other donor characteristics like age, sex/parity, additional HLA loci, etc. This leads to varying donor selection practices. Furthermore, the donor characteristic impact on mortality may differ from other critical outcomes. In this case study, we develop a general methodology for optimal donor selection with Bayesian nonparametric machine learning. Our findings suggest a simple clinical guideline: the youngest available male donor should be prioritized for all patients, except when there is a female donor considerably younger.
Authors 8
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Rodney Sparapani corresponding
Affiliation as printed
Division of Biostatistics, Medical College of Wisconsin
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European Society for Blood and Marrow Transplantation
Affiliation as printed
Center for International Blood and Marrow Transplant Research, NMDP
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European Society for Blood and Marrow Transplantation
Affiliation as printed
Center for International Blood and Marrow Transplant Research, NMDP
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European Society for Blood and Marrow Transplantation
Affiliation as printed
Center for International Blood and Marrow Transplant Research, NMDP
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Affiliation as printed
Center for International Blood and Marrow Transplant Research, Medical College of Wisconsin
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Affiliation as printed
Division of Biostatistics, Medical College of Wisconsin
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Affiliation as printed
Division of Biostatistics, Medical College of Wisconsin
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Affiliation as printed
Division of Biostatistics, Medical College of Wisconsin