Two dimensions of measurement error: Classical and Berkson error in residential radon exposure assessment

Abstract

Measurement error in exposure assessment is unavoidable. Statistical methods to correct for such errors rely upon a valid error model, particularly regarding the classification of classical and Berkson error, the structure and the size of the error. We provide a detailed list of sources of error in residential radon exposure assessment, stressing the importance of (a) the differentiation between classical and Berkson error and (b) the clear definitions of predictors and operationally defined predictors using the example of two German case-control studies on lung cancer and residential radon exposure. We give intuitive measures of error size and present evidence on both the error size and the multiplicative structure of the error from three data sets with repeated measurements of radon concentration. We conclude that modern exposure assessment should not only aim to be as accurate and precise as possible, but should also provide a model of the remaining measurement errors with clear differentiation of classical and Berkson components.