Assessing the causal effect of organ transplantation on the distribution of residual lifetime

David M Vock, Anastasios A. Tsiatis, Marie Davidian, Eric B. Laber, Wayne M. Tsuang, C. Ashley Finlen Copeland, Scott M. Palmer

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Summary: Because the number of patients waiting for organ transplants exceeds the number of organs available, a better understanding of how transplantation affects the distribution of residual lifetime is needed to improve organ allocation. However, there has been little work to assess the survival benefit of transplantation from a causal perspective. Previous methods developed to estimate the causal effects of treatment in the presence of time-varying confounders have assumed that treatment assignment was independent across patients, which is not true for organ transplantation. We develop a version of G-estimation that accounts for the fact that treatment assignment is not independent across individuals to estimate the parameters of a structural nested failure time model. We derive the asymptotic properties of our estimator and confirm through simulation studies that our method leads to valid inference of the effect of transplantation on the distribution of residual lifetime. We demonstrate our method on the survival benefit of lung transplantation using data from the United Network for Organ Sharing.

Original languageEnglish (US)
Pages (from-to)820-829
Number of pages10
JournalBiometrics
Volume69
Issue number4
DOIs
StatePublished - Dec 2013

Keywords

  • Causal inference
  • G-estimation
  • Lung transplantation
  • Martingale theory
  • Structural nested failure time models

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