Outcomes matter: Estimating pre-transplant survival rates of kidney-transplant patients using simulator-based propensity scores

Inbal Yahav, Galit Shmueli

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


The current kidney allocation system in the United States fails to match donors and recipients well. In an effort to improve the allocation system, the United Network of Organ Sharing (UNOS) defined factors that should determine a new allocation policy, and particularly patients' potential remaining lifetime without a transplant (pre-transplant survival rates). Estimating pre-transplant survival rates is challenging because the data available on candidates and organ recipients is already "contaminated" by the current allocation policy. In particular, the selection of patients who are offered (and decide to accept) a kidney is not random. We therefore expect differences in mortality-related characteristics of organ recipients and of candidates who have not received transplant. Such differences introduce bias into survival models. Existing approaches for tackling this selection bias either ignore the difference between candidates and recipients or assume that selection to transplant is based solely on patients' pre-transplant information, irrespective of the potential allocation outcome. We argue that in practice the allocation is dependent on the anticipated allocation outcome, which is a major factor in selection to transplant. Moreover, we show that ignoring the anticipated outcome increases model bias rather than decreases it. In this paper, we propose a novel simulator-based approach (SimBa) that adjusts for the selection bias by taking into account both pre-transplant and anticipated outcome information using simulation. We then fit survival models to kidney transplant waitlist data and compare the different adjustment methods. We find that SimBa not only fits the data best, but also captures a key aspect of the current allocation policy, namely, that the probability of kidney allocation increases in the expected pre-transplant life years.

Original languageEnglish
Pages (from-to)101-128
Number of pages28
JournalAnnals of Operations Research
Issue number1
StatePublished - May 2014

Bibliographical note

Funding Information:
This work was supported in part by Health Resources and Services Administration contract HHS/HRSA SRTR. The content is the responsibility of the authors alone and does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.


  • Kidney allocation
  • Pre-transplant survival rate
  • Propensity scores
  • Selection bias
  • Simulation
  • Survival analysis


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