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Doubly robust methods for handling confounding by cluster

Johan Zetterqvist, Stijn Vansteelandt UGent, Yudi Pawitan and Arvid Sjölander (2016) BIOSTATISTICS. 17(2). p.264-276
abstract
In clustered designs such as family studies, the exposure-outcome association is usually confounded by both cluster-constant and cluster-varying confounders. The influence of cluster-constant confounders can be eliminated by studying the exposure-outcome association within (conditional on) clusters, but additional regression modeling is usually required to control for observed cluster-varying confounders. A problem is that the working regression model may be misspecified, in which case the estimated within-cluster association may be biased. To reduce sensitivity to model misspecification we propose to augment the standard working model for the outcome with an auxiliary working model for the exposure. We derive a doubly robust conditional generalized estimating equation (DRCGEE) estimator for the within-cluster association. This estimator combines the two models in such a way that it is consistent if either model is correct, not necessarily both. Thus, the DRCGEE estimator gives the researcher two chances instead of only one to make valid inference on the within-cluster association. We have implemented the estimator in an R package and we use it to examine the association between smoking during pregnancy and cognitive abilities in offspring, in a sample of siblings.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
Conditional estimating equations, Doubly robust estimation, Epidemiology, G-estimation, Observational studies
journal title
BIOSTATISTICS
Biostatistics
volume
17
issue
2
pages
264 - 276
Web of Science type
Article
Web of Science id
000374235800005
JCR category
STATISTICS & PROBABILITY
JCR impact factor
1.798 (2016)
JCR rank
23/124 (2016)
JCR quartile
1 (2016)
ISSN
1465-4644
DOI
10.1093/biostatistics/kxv041
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
8507231
handle
http://hdl.handle.net/1854/LU-8507231
date created
2017-02-02 23:04:44
date last changed
2017-05-29 13:50:26
@article{8507231,
  abstract     = {In clustered designs such as family studies, the exposure-outcome association is usually confounded by both cluster-constant and cluster-varying confounders. The influence of cluster-constant confounders can be eliminated by studying the exposure-outcome association within (conditional on) clusters, but additional regression modeling is usually required to control for observed cluster-varying confounders. A problem is that the working regression model may be misspecified, in which case the estimated within-cluster association may be biased. To reduce sensitivity to model misspecification we propose to augment the standard working model for the outcome with an auxiliary working model for the exposure. We derive a doubly robust conditional generalized estimating equation (DRCGEE) estimator for the within-cluster association. This estimator combines the two models in such a way that it is consistent if either model is correct, not necessarily both. Thus, the DRCGEE estimator gives the researcher two chances instead of only one to make valid inference on the within-cluster association. We have implemented the estimator in an R package and we use it to examine the association between smoking during pregnancy and cognitive abilities in offspring, in a sample of siblings.},
  author       = {Zetterqvist, Johan and Vansteelandt, Stijn and Pawitan, Yudi and Sj{\"o}lander, Arvid},
  issn         = {1465-4644},
  journal      = {BIOSTATISTICS},
  keyword      = {Conditional estimating equations,Doubly robust estimation,Epidemiology,G-estimation,Observational studies},
  language     = {eng},
  number       = {2},
  pages        = {264--276},
  title        = {Doubly robust methods for handling confounding by cluster},
  url          = {http://dx.doi.org/10.1093/biostatistics/kxv041},
  volume       = {17},
  year         = {2016},
}

Chicago
Zetterqvist, Johan, Stijn Vansteelandt, Yudi Pawitan, and Arvid Sjölander. 2016. “Doubly Robust Methods for Handling Confounding by Cluster.” Biostatistics 17 (2): 264–276.
APA
Zetterqvist, J., Vansteelandt, S., Pawitan, Y., & Sjölander, A. (2016). Doubly robust methods for handling confounding by cluster. BIOSTATISTICS, 17(2), 264–276.
Vancouver
1.
Zetterqvist J, Vansteelandt S, Pawitan Y, Sjölander A. Doubly robust methods for handling confounding by cluster. BIOSTATISTICS. 2016;17(2):264–76.
MLA
Zetterqvist, Johan, Stijn Vansteelandt, Yudi Pawitan, et al. “Doubly Robust Methods for Handling Confounding by Cluster.” BIOSTATISTICS 17.2 (2016): 264–276. Print.