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

• Published in: PloS one 2018-02, Vol.13 (2), p.e0190792-e0190792
• Main Authors: Hoffmann, Sabine, Laurier, Dominique, Rage, Estelle, Guihenneuc, Chantal, Ancelet, Sophie
• Format: Article
• Language: English
• Subjects: Cohort analysis; Computer simulation; Dose-response effects; Engineering and Technology; Epidemiology; Error analysis; Exposure; Health aspects; Health risk assessment; Life Sciences; Lung cancer; Medical research; Medicine; Medicine and Health Sciences; Methods; Miners; Mining; Mortality; Observational studies; Occupational exposure; Occupational hazards; Physical Sciences; Radon; Research and Analysis Methods; Risk; Risk factors; Social Sciences; Statistical analysis; Statistical inference; Statistical models; Studies; Uncertainty; Uranium; Workers
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0190792

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.