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Doubly robust estimation of attributable fractions in survival analysis

Arvid Sjolander and Stijn Vansteelandt UGent (2017) STATISTICAL METHODS IN MEDICAL RESEARCH. 26(2). p.948-969
abstract
The attributable fraction is a commonly used measure that quantifies the public health impact of an exposure on an outcome. It was originally defined for binary outcomes, but an extension has recently been proposed for right-censored survival time outcomes; the so-called attributable fraction function. A maximum likelihood estimator of the attributable fraction function has been developed, which requires a model for the outcome. In this paper, we derive a doubly robust estimator of the attributable fraction function. This estimator requires one model for the outcome, and one joint model for the exposure and censoring. The estimator is consistent if either model is correct, not necessarily both.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
Attributable fraction, causal inference, cohort studies, Cox proportional hazards model, doubly robust estimation, CAUSAL INFERENCE, AUTISM, EVENT
journal title
STATISTICAL METHODS IN MEDICAL RESEARCH
Stat. Methods Med. Res.
volume
26
issue
2
pages
948 - 969
Web of Science type
Article
Web of Science id
000399704500025
ISSN
0962-2802
1477-0334
DOI
10.1177/0962280214564003
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
8545454
handle
http://hdl.handle.net/1854/LU-8545454
date created
2018-01-18 14:45:46
date last changed
2018-02-13 14:43:07
@article{8545454,
  abstract     = {The attributable fraction is a commonly used measure that quantifies the public health impact of an exposure on an outcome. It was originally defined for binary outcomes, but an extension has recently been proposed for right-censored survival time outcomes; the so-called attributable fraction function. A maximum likelihood estimator of the attributable fraction function has been developed, which requires a model for the outcome. In this paper, we derive a doubly robust estimator of the attributable fraction function. This estimator requires one model for the outcome, and one joint model for the exposure and censoring. The estimator is consistent if either model is correct, not necessarily both.},
  author       = {Sjolander, Arvid and Vansteelandt, Stijn},
  issn         = {0962-2802},
  journal      = {STATISTICAL METHODS IN MEDICAL RESEARCH},
  keyword      = {Attributable fraction,causal inference,cohort studies,Cox proportional hazards model,doubly robust estimation,CAUSAL INFERENCE,AUTISM,EVENT},
  language     = {eng},
  number       = {2},
  pages        = {948--969},
  title        = {Doubly robust estimation of attributable fractions in survival analysis},
  url          = {http://dx.doi.org/10.1177/0962280214564003},
  volume       = {26},
  year         = {2017},
}

Chicago
Sjolander, Arvid, and Stijn Vansteelandt. 2017. “Doubly Robust Estimation of Attributable Fractions in Survival Analysis.” Statistical Methods in Medical Research 26 (2): 948–969.
APA
Sjolander, Arvid, & Vansteelandt, S. (2017). Doubly robust estimation of attributable fractions in survival analysis. STATISTICAL METHODS IN MEDICAL RESEARCH, 26(2), 948–969.
Vancouver
1.
Sjolander A, Vansteelandt S. Doubly robust estimation of attributable fractions in survival analysis. STATISTICAL METHODS IN MEDICAL RESEARCH. 2017;26(2):948–69.
MLA
Sjolander, Arvid, and Stijn Vansteelandt. “Doubly Robust Estimation of Attributable Fractions in Survival Analysis.” STATISTICAL METHODS IN MEDICAL RESEARCH 26.2 (2017): 948–969. Print.