5 research outputs found

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

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    <div><p>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.</p></div

    Estimated exposure-response curve when fitting the Excess Hazard Ratio (EHR) model based on natural cubic splines when data are generated according to the EHR model with a risk coefficient of <i>β</i> = 5.

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    <p>(a) , i.e., no measurement error (b) , i.e., unshared and homoscedastic Berkson error, (c) , i.e., unshared error of Berkson and classical type (d) , i.e., heteroscedastic error with a shared classical component describing the imprecision of the measurement device and (e) , i.e., heteroscedastic error with a shared Berkson component describing individual worker practices.</p

    Comparison of risk estimates when data are generated according to different disease and measurement models.

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    <p>DIC<sub><i>EHR</i></sub> < DIC<sub><i>Cox</i></sub> gives the percentage of realisations for which the Deviance Information Criterion (DIC) was smaller for the Excess Hazard Ratio (EHR) model when the true model was the Cox model and vice versa for DIC<sub><i>Cox</i></sub> < DIC<sub><i>EHR</i></sub>. The difference in DIC is calculated as difference between the EHR model and the Cox model.</p

    Estimated exposure-response curve when fitting the Cox model based on natural cubic splines when data are generated according to the Cox model with a risk coefficient of <i>β</i> = 2.

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    <p>(a) , i.e., no measurement error (b) , i.e., unshared and homoscedastic Berkson error, (c) , i.e., unshared error of Berkson and classical type (d) , i.e., heteroscedastic error with a shared classical component describing the imprecision of the measurement device and (e) , i.e., heteroscedastic error with a shared Berkson component describing individual worker practices.</p

    Average posterior median (), overall 95% credible intervals (CI<sub>95%</sub>), relative bias and coverage rate for 100 data sets generated according to the EHR model , a measurement model among to and a true risk coefficient of <i>β</i> = 5 per 100 WLM.

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    <p>Average posterior median (), overall 95% credible intervals (CI<sub>95%</sub>), relative bias and coverage rate for 100 data sets generated according to the EHR model , a measurement model among to and a true risk coefficient of <i>β</i> = 5 per 100 WLM.</p
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