Statistical modelling for exposure measurement error with application to epidemiological data

Background Measurement error in exposure variables is an important issue in epidemiological studies that relate exposures to health outcomes. Such studies, however, usually pay limited attention to the quantitative effects of exposure measurement error on estimated exposure-outcome associations. Therefore, the estimators for exposure-outcome associations are prone to bias. Existing methods to adjust for the bias in the associations require a validation study with multiple replicates of a reference measurement. Validation studies with multiple replicates are quite costly and therefore, in some cases only a single–replicate validation study is conducted besides the main study. For a study that does not include an internal validation study, the challenge in dealing with exposure measurement error is even bigger. The challenge is how to use external data from other similar validation studies to adjust for the bias in the exposure-outcome association. In accelerometry research, various accelerometer models have currently been developed. However, some of these new accelerometer models have not been properly validated in field situations. Despite the widely recognized measurement error in the accelerometer, some accelerometers have been used to validate other instruments, such as physical activity questionnaires, in measuring physical activity. Consequently, if an instrument is validated against the accelerometer, and the accelerometer itself has considerable measurement error, the observed validity in the instrument being validated will misrepresent the true validity. Methodology In this thesis, we adapted regression calibration to adjust for exposure measurement error for a single-replicate validation study with zero-inflated reference measurements and assessed the adequacy of the adapted method in a simulation study. For the case where there is no internal validation study, we showed how to combine external data on validity for self-report instruments with the observed