Advanced search
1 file | 452.30 KB Add to list

Instrumental variables estimation with competing risk data

(2020) BIOSTATISTICS. 21(1). p.158-171
Author
Organization
Abstract
Time-to-event analyses are often plagued by both-possibly unmeasured-confounding and competing risks. To deal with the former, the use of instrumental variables (IVs) for effect estimation is rapidly gaining ground. We show how to make use of such variables in competing risk analyses. In particular, we show how to infer the effect of an arbitrary exposure on cause-specific hazard functions under a semi-parametric model that imposes relatively weak restrictions on the observed data distribution. The proposed approach is flexible accommodating exposures and IVs of arbitrary type, and enabling covariate adjustment. It makes use of closed-form estimators that can be recursively calculated, and is shown to perform well in simulation studies. We also demonstrate its use in an application on the effect of mammography screening on the risk of dying from breast cancer.
Keywords
Statistics, Probability and Uncertainty, Statistics and Probability, General Medicine, RANDOMIZED-TRIALS, NONCOMPLIANCE, Causal effect, Competing risk, Instrumental variable, Time-to-event, Unobserved confounding

Downloads

  • (...).pdf
    • full text (Published version)
    • |
    • UGent only
    • |
    • PDF
    • |
    • 452.30 KB

Citation

Please use this url to cite or link to this publication:

MLA
Martinussen, Torben, and Stijn Vansteelandt. “Instrumental Variables Estimation with Competing Risk Data.” BIOSTATISTICS, vol. 21, no. 1, 2020, pp. 158–71.
APA
Martinussen, T., & Vansteelandt, S. (2020). Instrumental variables estimation with competing risk data. BIOSTATISTICS, 21(1), 158–171.
Chicago author-date
Martinussen, Torben, and Stijn Vansteelandt. 2020. “Instrumental Variables Estimation with Competing Risk Data.” BIOSTATISTICS 21 (1): 158–71.
Chicago author-date (all authors)
Martinussen, Torben, and Stijn Vansteelandt. 2020. “Instrumental Variables Estimation with Competing Risk Data.” BIOSTATISTICS 21 (1): 158–171.
Vancouver
1.
Martinussen T, Vansteelandt S. Instrumental variables estimation with competing risk data. BIOSTATISTICS. 2020;21(1):158–71.
IEEE
[1]
T. Martinussen and S. Vansteelandt, “Instrumental variables estimation with competing risk data,” BIOSTATISTICS, vol. 21, no. 1, pp. 158–171, 2020.
@article{8591110,
  abstract     = {Time-to-event analyses are often plagued by both-possibly unmeasured-confounding and competing risks. To deal with the former, the use of instrumental variables (IVs) for effect estimation is rapidly gaining ground. We show how to make use of such variables in competing risk analyses. In particular, we show how to infer the effect of an arbitrary exposure on cause-specific hazard functions under a semi-parametric model that imposes relatively weak restrictions on the observed data distribution. The proposed approach is flexible accommodating exposures and IVs of arbitrary type, and enabling covariate adjustment. It makes use of closed-form estimators that can be recursively calculated, and is shown to perform well in simulation studies. We also demonstrate its use in an application on the effect of mammography screening on the risk of dying from breast cancer.},
  author       = {Martinussen, Torben and Vansteelandt, Stijn},
  issn         = {1465-4644},
  journal      = {BIOSTATISTICS},
  keywords     = {Statistics,Probability and Uncertainty,Statistics and Probability,General Medicine,RANDOMIZED-TRIALS,NONCOMPLIANCE,Causal effect,Competing risk,Instrumental variable,Time-to-event,Unobserved confounding},
  language     = {eng},
  number       = {1},
  pages        = {158--171},
  title        = {Instrumental variables estimation with competing risk data},
  url          = {http://dx.doi.org/10.1093/biostatistics/kxy039},
  volume       = {21},
  year         = {2020},
}

Altmetric
View in Altmetric
Web of Science
Times cited: