Abstract
Exposure measurement error represents one of the most important sources of uncertainty in epidemiology. When exposure uncertainty is not or only poorly accounted for, it can lead to biased risk estimates and a distortion of the shape of the exposure-response relationship. In occupational cohort studies, the time-dependent nature of exposure and changes in the method of exposure assessment may create complex error structures. When a method of group-level exposure assessment is used, individual worker practices and the imprecision of the instrument used to measure the average exposure for a group of workers may give rise to errors that are shared between workers, within workers or both. In contrast to unshared measurement error, the effects of shared errors remain largely unknown. Moreover, exposure uncertainty and magnitude of exposure are typically highest for the earliest years of exposure. We conduct a simulation study based on exposure data of the French cohort of uranium miners to compare the effects of shared and unshared exposure uncertainty on risk estimation and on the shape of the exposure-response curve in proportional hazards models. Our results indicate that uncertainty components shared within workers cause more bias in risk estimation and a more severe attenuation of the exposure-response relationship than unshared exposure uncertainty or exposure uncertainty shared between individuals. These findings underline the importance of careful characterisation and modeling of exposure uncertainty in observational studies.
Highlights
Exposure measurement error is arguably one of the most important sources of uncertainty in epidemiological studies
In contrast to claims that uncertainty shared between individuals should have fundamentally different effects on parameter estimation than unshared exposure uncertainty [15, 16], we found that both error components resulted in comparable relative bias and coverage rates in risk estimation in proportional hazard models
In line with this argument, concerning the relative bias in risk estimates in the presence of large and moderate unshared Berkson error, we observed values that were consistent with the results of Bender et al (2005) and Kuchenhoff et al (2007), who studied the effect of unshared additive and multiplicative Berkson error on frequentist inference conducted for the Cox model
Summary
Exposure measurement error is arguably one of the most important sources of uncertainty in epidemiological studies. It is widely acknowledged that when it is not or only poorly accounted for, measurement error can lead to biased risk estimates, a distortion of the shape of the exposure-response relationship and a loss in statistical power [1, 2]. Shared and unshared error in occupational cohort studies and their effects on statistical inference the Universite Paris Descartes. There was no additional external funding received for this study
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