Skip Navigation

Biometrika 1993 80(4):807-815; doi:10.1093/biomet/80.4.807
© 1993 by Biometrika Trust
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by NEUHAUS, J. M.
Right arrow Articles by JEWELL, N. P.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

A geometric approach to assess bias due to omitted covariates in generalized linear models

JOHN M. NEUHAUS1 and NICHOLAS P. JEWELL2

1Department of Epidemiology and Biostatistics, University of California San Francisco, California 94143–0560, U.S.A.
2Group in Biostatistics, University of California Berkeley, California 947 20, U.S.A.

Misspecifying generalized linear models by omitting covariates associated with the response can result in seriously biased estimates of the effects of the included covariates. For certain models, such bias can occur even when the omitted covariate is independent of the included covariates, as in studies with randomized treatment assignment. This paper presents a geometric approach to assess the direction of the bias resulting from omitted covariates and develop intuition about the effects of omitted covariates. We show that the direction of the bias depends simply on geometric properties of the link function. We present an expression for the magnitude of the bias and the results of simulations which corroborate these findings. We also consider an extension of the methods to assess the effects of omitted covariates which are correlated with those included.

Key Words: Attenuation • Concave function • Link function


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BiostatisticsHome page
G. Verbeke and S. Fieuws
The effect of miss-specified baseline characteristics on inference for longitudinal trends in linear mixed models
Biostat., October 1, 2007; 8(4): 772 - 783.
[Abstract] [Full Text] [PDF]

Home page
 Med Care Res RevHome page
S.-Y. Liang, K. A. Phillips, and J. S. Haas
Measuring managed care and its environment using national surveys: a review and assessment.
Med Care Res Rev, December 1, 2006; 63(6 Suppl): 9S - 36S.
[Abstract] [PDF]

Home page
 Int J EpidemiolHome page
C. Frost and I. R White
The effect of measurement error in risk factors that change over time in cohort studies: do simple methods overcorrect for 'regression dilution'?
Int. J. Epidemiol., December 1, 2005; 34(6): 1359 - 1368.
[Abstract] [Full Text] [PDF]

Home page
 Stat Methods Med ResHome page
I. R White
Uses and limitations of randomization-based efficacy estimators
Statistical Methods in Medical Research, August 1, 2005; 14(4): 327 - 347.
[Abstract] [PDF]

Home page
 Am J EpidemiolHome page
G. Zou
A Modified Poisson Regression Approach to Prospective Studies with Binary Data
Am. J. Epidemiol., April 1, 2004; 159(7): 702 - 706.
[Abstract] [Full Text] [PDF]

Home page
 Stat Methods Med ResHome page
N. Breslow, B. Leroux, and R. Platt
Approximate hierarchical modelling of discrete data in epidemiology
Statistical Methods in Medical Research, February 1, 1998; 7(1): 49 - 62.
[Abstract] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.