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Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models

Torben Martinussen, Stijn Vansteelandt UGent, Eric J Tchetgen Tchetgen and David M Zucker (2017) BIOMETRICS. 73(4). p.1140-1149
abstract
The use of instrumental variables for estimating the effect of an exposure on an outcome is popular in econometrics, and increasingly so in epidemiology. This increasing popularity may be attributed to the natural occurrence of instrumental variables in observational studies that incorporate elements of randomization, either by design or by nature (e.g., random inheritance of genes). Instrumental variables estimation of exposure effects is well established for continuous outcomes and to some extent for binary outcomes. It is, however, largely lacking for time-to-event outcomes because of complications due to censoring and survivorship bias. In this article, we make a novel proposal under a class of structural cumulative survival models which parameterize time-varying effects of a point exposure directly on the scale of the survival function; these models are essentially equivalent with a semi-parametric variant of the instrumental variables additive hazards model. We propose a class of recursive instrumental variable estimators for these exposure effects, and derive their large sample properties along with inferential tools. We examine the performance of the proposed method in simulation studies and illustrate it in a Mendelian randomization study to evaluate the effect of diabetes on mortality using data from the Health and Retirement Study. We further use the proposed method to investigate potential benefit from breast cancer screening on subsequent breast cancer mortality based on the HIP-study.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
Causal effect, Confounding, Current treatment interaction, G-estimation, Instrumental variable, Mendelian randomization, REGRESSION-MODELS, CAUSAL INFERENCE, HAZARDS MODELS, NONCOMPLIANCE, TRIALS, CANCER, BIAS
journal title
BIOMETRICS
Biometrics
volume
73
issue
4
pages
1140 - 1149
Web of Science type
Article
Web of Science id
000418854100008
ISSN
0006-341X
1541-0420
DOI
10.1111/biom.12699
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
8545676
handle
http://hdl.handle.net/1854/LU-8545676
date created
2018-01-22 09:35:56
date last changed
2018-02-23 08:21:41
@article{8545676,
  abstract     = {The use of instrumental variables for estimating the effect of an exposure on an outcome is popular in econometrics, and increasingly so in epidemiology. This increasing popularity may be attributed to the natural occurrence of instrumental variables in observational studies that incorporate elements of randomization, either by design or by nature (e.g., random inheritance of genes). Instrumental variables estimation of exposure effects is well established for continuous outcomes and to some extent for binary outcomes. It is, however, largely lacking for time-to-event outcomes because of complications due to censoring and survivorship bias. In this article, we make a novel proposal under a class of structural cumulative survival models which parameterize time-varying effects of a point exposure directly on the scale of the survival function; these models are essentially equivalent with a semi-parametric variant of the instrumental variables additive hazards model. We propose a class of recursive instrumental variable estimators for these exposure effects, and derive their large sample properties along with inferential tools. We examine the performance of the proposed method in simulation studies and illustrate it in a Mendelian randomization study to evaluate the effect of diabetes on mortality using data from the Health and Retirement Study. We further use the proposed method to investigate potential benefit from breast cancer screening on subsequent breast cancer mortality based on the HIP-study.},
  author       = {Martinussen, Torben and Vansteelandt, Stijn and Tchetgen, Eric J Tchetgen and Zucker, David M},
  issn         = {0006-341X},
  journal      = {BIOMETRICS},
  keyword      = {Causal effect,Confounding,Current treatment interaction,G-estimation,Instrumental variable,Mendelian randomization,REGRESSION-MODELS,CAUSAL INFERENCE,HAZARDS MODELS,NONCOMPLIANCE,TRIALS,CANCER,BIAS},
  language     = {eng},
  number       = {4},
  pages        = {1140--1149},
  title        = {Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models},
  url          = {http://dx.doi.org/10.1111/biom.12699},
  volume       = {73},
  year         = {2017},
}

Chicago
Martinussen, Torben, Stijn Vansteelandt, Eric J Tchetgen Tchetgen, and David M Zucker. 2017. “Instrumental Variables Estimation of Exposure Effects on a Time-to-event Endpoint Using Structural Cumulative Survival Models.” Biometrics 73 (4): 1140–1149.
APA
Martinussen, T., Vansteelandt, S., Tchetgen, E. J. T., & Zucker, D. M. (2017). Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models. BIOMETRICS, 73(4), 1140–1149.
Vancouver
1.
Martinussen T, Vansteelandt S, Tchetgen EJT, Zucker DM. Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models. BIOMETRICS. 2017;73(4):1140–9.
MLA
Martinussen, Torben, Stijn Vansteelandt, Eric J Tchetgen Tchetgen, et al. “Instrumental Variables Estimation of Exposure Effects on a Time-to-event Endpoint Using Structural Cumulative Survival Models.” BIOMETRICS 73.4 (2017): 1140–1149. Print.