Bias and efficiency loss due to misclassified responses in binary regression

Methods that ignore errors in binary responses yield biased estimators of the associations of covariates with response. This paper derives general expressions for the magnitude of the bias due to errors in the response and shows that, unless both the sensitivity and specificity are very high, ignori...

Full description

Saved in:
Bibliographic Details
Published in:Biometrika 1999-12, Vol.86 (4), p.843-855
Main Author: Neuhaus, John M.
Format: Article
Language:English
Subjects:
Associations
asymptotic relative efficiency
attenuation
Bias
Consistent estimators
Efficiency
Efficiency loss
Error rates
Estimates
Estimation bias
Estimators
Exact sciences and technology
generalised linear model
Human papillomavirus
Infections
Linear inference, regression
Linear models
Logistics
Mathematics
Maximum likelihood estimation
misspecified link function
misspecified model
Probability
Probability and statistics
Regression analysis
Sciences and techniques of general use
Statistics
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Methods that ignore errors in binary responses yield biased estimators of the associations of covariates with response. This paper derives general expressions for the magnitude of the bias due to errors in the response and shows that, unless both the sensitivity and specificity are very high, ignoring errors in the responses will yield highly biased covariate effect estimators. When the true, error-free response follows a generalised linear model and misclassification probabilities are known and independent of covariate values, responses observed with error also follow such a model with a modified link function. We describe a simple method to obtain consistent estimators of covariate effects and associated errors in this case, and derive an expression for the asymptotic relative efficiency of covariate effect estimators from the correct likelihood for the responses with errors with respect to estimates based on the true, error-free responses. This expression shows that errors in the response can lead to substantial losses of information about covariate effects. Data from a study on infection with human papilloma virus among women and simulation studies motivate this work and illustrate the findings.
ISSN:0006-3444
1464-3510
DOI:10.1093/biomet/86.4.843