Measurement error in the explanatory variable of a binary regression: regression calibration and integrated conditional likelihood in studies of residential radon and lung cancer.

Journal article

In epidemiology, one approach to investigating the dependence of disease risk on an explanatory variable in the presence of several confounding variables is by fitting a binary regression using a conditional likelihood, thus eliminating the nuisance parameters. When the explanatory variable is measured with error, the estimated regression coefficient is biased usually towards zero. Motivated by the need to correct for this bias in analyses that combine data from a number of case-control studi...

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Authors: Fearn, T; Hill, D; Darby, S

• Publication status: Published
• Journal: Statistics in medicine
• Volume: 27
• Issue: 12
• Pages: 2159-2176
• Publication date: 2008-05-01
• DOI: 10.1002/sim.3163
• EISSN: 1097-0258
• ISSN: 0277-6715
• Language: English
• Keywords: Regression Analysis; Case-Control Studies; Humans; Radon; Bias (Epidemiology); Housing; Lung Neoplasms; Models, Statistical; Likelihood Functions