Assessing the Effect of Organ Transplantation on the Distribution of Residual Lifetime

• Activity Number: 214
• Type: Invited
• Date/Time: Monday, August 5, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): David Michael Vock*+ and Anastasios (Butch) Tsiatis and Marie Davidian and Eric Laber and Wayne M Tsuang and C. Ashley Finlen Copeland and Scott M. Palmer
• Companies: University of Minnesota and North Carolina State University and North Carolina State University and NC State University and Duke University and Duke University and Duke University
• Keywords: Causal Inference ; G-Estimation ; Lung Transplantation ; Martingale Theory ; Structural Nested Failure Time Models

Abstract

An understanding of how transplantation affects 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. In addition, G-estimation for failure time models requires the use of artificial censoring, a technique where some subjects observed to fail are censored. We suggest some computational strategies to mitigate the problems typically encountered with artificial censoring. We derive the asymptotic properties of our estimator and confirm through simulation studies that our method leads to valid inference on the effect of transplantation on the distribution of residual lifetime. We demonstrate our method on the survival benefit of lung transplantation.