A Note on the Bias of Estimators with Missing Data

It is well known that many standard analyses, including maximum likelihood estimation and the generalized estimating equation approach (Liang and Zeger, 1986, Biometrika 73, 13-22) can result in biased estimation when there are missing observations. In such cases it is of interest to calculate the m...

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Bibliographic Details
Published in:Biometrics 1994-12, Vol.50 (4), p.1163-1170
Main Authors: Rotnitzky, Andrea, Wypij, David
Format: Article
Language:English
Subjects:
Asthma
Asthma - epidemiology
Bias
Biometry - methods
Child
Children
Cohort Studies
Consistent estimators
Covariance
Estimation bias
Estimation methods
Estimators
Female
Gender bias
Humans
Longitudinal Studies
Male
Maximum likelihood estimation
Missing data
Models, Statistical
Ohio - epidemiology
Prevalence
Regression Analysis
Shorter Communications
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Summary:It is well known that many standard analyses, including maximum likelihood estimation and the generalized estimating equation approach (Liang and Zeger, 1986, Biometrika 73, 13-22) can result in biased estimation when there are missing observations. In such cases it is of interest to calculate the magnitude of the bias incurred under specific assumptions about the process generating the full data and the nonresponse mechanism. In this paper we give a condition that identifies the limit in probability of estimators that are solutions of estimating equations computed from the incomplete data. With discrete data, this condition suggests a simple algorithm to compute the asymptotic bias of these estimators that can be easily implemented with existing statistical software. We illustrate our approach with asthma prevalence data in children.
ISSN:0006-341X
1541-0420
DOI:10.2307/2533454