Shared and unshared exposure measurement error in occupational cohort studies and their effects on statistical inference in proportional hazards models

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.

PUMA – pooled uranium miners analysis: cohort profile

“…This is much more feasible in PUMA than in earlier pooled analyses1 because the PUMA study encompasses relatively long-term follow-up of workers employed in more recent periods of mine operation for whom we have a more accurate exposure assessment. Of course, restriction is not the only approach to dealing with potential bias due to exposure measurement error; we also can leverage insights from recent methodological work on impacts and potential corrections for exposures measurement errors that make use of all available data 40–42…”

Exposure measurement error in air pollution studies: A framework for assessing shared, multiplicative measurement error in ensemble learning estimates of nitrogen oxides

“…We observed significantly greater SMME in the earliest years (1992–2000) compared to later years (> 2001). This decreasing temporal pattern in the uncertainties is common in retrospective exposure reconstructions ( Hoffmann et al, 2018 ) and may be the result of measurement methods improving or changing over time (for example, a shift from using Palmes tubes to Ogawa badges for passive NO x monitoring). The underlying data in the model inputs or covariates may have also become more accurate or complete over time.…”

“…SUMA methods also do not account for “between shared error” attributable to time, for example, predictions made in the same year and month will share uncertainties. Previous simulation studies determined that shared error within predictions resulted in greater bias than shared error between predictions ( Hoffmann et al, 2018 ). We hope to elaborate on SUMA models to enable classification of within and between shared errors in future work.…”

“…Our methods can help improve the development of future exposure models by either highlighting areas in space or periods in time where more refined data or methods are needed or shedding light on potentially important inputs or predictors that might be overlooked. Further, characterization of exposure errors can be used to improve confidence in epidemiological inference ( Hoffmann et al, 2018 ) through adjustment of confidence intervals to account for SMME ( Stram and Kopecky, 2003 ) or attenuation of the dose response curve ( Stram et al, 2015 ). Given the importance of this work to exposure science and environmental epidemiology, our follow up work will focus on assessing the impact of SUMA exposure error on epidemiological health estimates and methods for adjusting them accordingly.…”

Current intelligence bulletin 69: NIOSH practices in occupational risk assessment.

“…Measurement error can be shared or unshared [Hoffmann et al 2018]. If the error in dose among individuals, groups, and time is independent and identically distributed (a general assumption in most error structure models), the error is unshared.…”