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 journalArticle

7 Citations (Scopus)

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 1 2013

Fingerprint

Transplantation (surgical)
Residual Lifetime
organ transplantation
Causal Effect
Transplants
Transplantation
Organ Transplantation
Survival
Lung Transplantation
Assignment
Therapeutics
methodology
lungs
Failure Time
Lung
Estimate
Asymptotic Properties
Time-varying
Exceed
Sharing

Keywords

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

Cite this

Vock, D. M., Tsiatis, A. A., Davidian, M., Laber, E. B., Tsuang, W. M., Finlen Copeland, C. A., & Palmer, S. M. (2013). Assessing the causal effect of organ transplantation on the distribution of residual lifetime. Biometrics, 69(4), 820-829. https://doi.org/10.1111/biom.12084

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 journalArticle

Vock, DM, Tsiatis, AA, Davidian, M, Laber, EB, Tsuang, WM, Finlen Copeland, CA & Palmer, SM 2013, 'Assessing the causal effect of organ transplantation on the distribution of residual lifetime', Biometrics, vol. 69, no. 4, pp. 820-829. https://doi.org/10.1111/biom.12084
Vock DM, Tsiatis AA, Davidian M, Laber EB, Tsuang WM, Finlen Copeland CA et al. Assessing the causal effect of organ transplantation on the distribution of residual lifetime. Biometrics. 2013 Dec 1;69(4):820-829. https://doi.org/10.1111/biom.12084
Vock, David M ; Tsiatis, Anastasios A. ; Davidian, Marie ; Laber, Eric B. ; Tsuang, Wayne M. ; Finlen Copeland, C. Ashley ; Palmer, Scott M. / Assessing the causal effect of organ transplantation on the distribution of residual lifetime. In: Biometrics. 2013 ; Vol. 69, No. 4. pp. 820-829.
@article{ab11f4c36192436392fdbcc1feff7564,
title = "Assessing the causal effect of organ transplantation on the distribution of residual lifetime",
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.",
keywords = "Causal inference, G-estimation, Lung transplantation, Martingale theory, Structural nested failure time models",
author = "Vock, {David M} and Tsiatis, {Anastasios A.} and Marie Davidian and Laber, {Eric B.} and Tsuang, {Wayne M.} and {Finlen Copeland}, {C. Ashley} and Palmer, {Scott M.}",
year = "2013",
month = "12",
day = "1",
doi = "10.1111/biom.12084",
language = "English (US)",
volume = "69",
pages = "820--829",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "4",

}

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 -