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 language | English (US) |
|---|---|
| Pages (from-to) | 820-829 |
| Number of pages | 10 |
| Journal | Biometrics |
| Volume | 69 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 1 2013 |
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Keywords
- Causal inference
- G-estimation
- Lung transplantation
- Martingale theory
- Structural nested failure time models
Cite this
Assessing the causal effect of organ transplantation on the distribution of residual lifetime. / Vock, David M; Tsiatis, Anastasios A.; Davidian, Marie; Laber, Eric B.; Tsuang, Wayne M.; Finlen Copeland, C. Ashley; Palmer, Scott M.
In: Biometrics, Vol. 69, No. 4, 01.12.2013, p. 820-829.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Assessing the causal effect of organ transplantation on the distribution of residual lifetime
AU - Vock, David M
AU - Tsiatis, Anastasios A.
AU - Davidian, Marie
AU - Laber, Eric B.
AU - Tsuang, Wayne M.
AU - Finlen Copeland, C. Ashley
AU - Palmer, Scott M.
PY - 2013/12/1
Y1 - 2013/12/1
N2 - 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.
AB - 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.
KW - Causal inference
KW - G-estimation
KW - Lung transplantation
KW - Martingale theory
KW - Structural nested failure time models
UR - http://www.scopus.com/inward/record.url?scp=84890313519&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890313519&partnerID=8YFLogxK
U2 - 10.1111/biom.12084
DO - 10.1111/biom.12084
M3 - Article
C2 - 24128090
AN - SCOPUS:84890313519
VL - 69
SP - 820
EP - 829
JO - Biometrics
JF - Biometrics
SN - 0006-341X
IS - 4
ER -