Abstract
Exposure measurement error is a problem in many epidemiological studies, including those using biomarkers and measures of dietary intake. Measurement error typically results in biased estimates of exposure-disease associations, the severity and nature of the bias depending on the form of the error. To correct for the effects of measurement error, information additional to the main study data is required. Ideally, this is a validation sample in which the true exposure is observed. However, in many situations, it is not feasible to observe the true exposure, but there may be available one or more repeated exposure measurements, for example, blood pressure or dietary intake recorded at two time points. The aim of this paper is to provide a toolkit for measurement error correction using repeated measurements. We bring together methods covering classical measurement error and several departures from classical error: systematic, heteroscedastic and differential error. The correction methods considered are regression calibration, which is already widely used in the classical error setting, and moment reconstruction and multiple imputation, which are newer approaches with the ability to handle differential error. We emphasize practical application of the methods in nutritional epidemiology and other fields. We primarily consider continuous exposures in the exposure-outcome model, but we also outline methods for use when continuous exposures are categorized. The methods are illustrated using the data from a study of the association between fibre intake and colorectal cancer, where fibre intake is measured using a diet diary and repeated measures are available for a subset. © 2014 The Authors.
Highlights
The vector of adjustment variables is denoted z. Note that it is sufficient in this example for z.m to contain only the information on sex because exact age appears as an adjustment variable
To perform Regression calibration (RC) we first fit the RC model, which is a regression of the second exposure measurement on the first and on all adjustment variables including the matching variables: rc.model
The variance of the corrected log odds ratio estimate is underestimated in the above model because it does not take into account the uncertainty in the parameters estimated in the regression calibration model
Summary
The naive analysis is performed as follows: library(survival) naive.analysis
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