Abstract:
: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.
journal_name
Biometricsjournal_title
Biometricsauthors
Vock DM,Tsiatis AA,Davidian M,Laber EB,Tsuang WM,Finlen Copeland CA,Palmer SMdoi
10.1111/biom.12084subject
Has Abstractpub_date
2013-12-01 00:00:00pages
820-9issue
4eissn
0006-341Xissn
1541-0420journal_volume
69pub_type
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