Correction of confidence intervals in excess relative risk models using Monte Carlo dosimetry systems with shared errors

Abstract

In epidemiological studies, exposures of interest are often measured with uncertainties, which may be independent or correlated. Independent errors can often be characterized relatively easily while correlated measurement errors have shared and hierarchical components that complicate the description of their structure. For some important studies, Monte Carlo dosimetry systems that provide multiple realizations of exposure estimates have been used to represent such complex error structures. While the effects of independent measurement errors on parameter estimation and methods to correct these effects have been studied comprehensively in the epidemiological literature, the literature on the effects of correlated errors, and associated correction methods is much more sparse. In this paper, we implement a novel method that calculates corrected confidence intervals based on the approximate asymptotic distribution of parameter estimates in linear excess relative risk (ERR) models. These models are widely used in survival analysis, particularly in radiation epidemiology. Specifically, for the dose effect estimate of interest (increase in relative risk per unit dose), a mixture distribution consisting of a normal and a lognormal component is applied. This choice of asymptotic approximation guarantees that corrected confidence intervals will always be bounded, a result which does not hold under a normal approximation. A simulation study was conducted to evaluate the proposed method in survival analysis using a realistic ERR model. We used both simulated Monte Carlo dosimetry systems (MCDS) and actual dose histories from the Mayak Worker Dosimetry System 2013, a MCDS for plutonium exposures in the Mayak Worker Cohort. Results show our proposed methods provide much improved coverage probabilities for the dose effect parameter, and noticeable improvements for other model parameters.

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

“…Although we found that shared additive error was larger in magnitude than SMME, we focused our analysis on SMME as other work has indicated minimal influence of shared additive error on epidemiological results in a Berkson model ( Zhang et al, 2017 ). Shared error differs from traditional measurement error as the errors are not independent, which is common in air pollution exposure models because (1) model covariates are usually aggregated in time and space and (2) air pollution exhibits finely resolved variability through time and space.…”

Organ Doses from Chest Radiographs in Tuberculosis Patients in Canada and Their Uncertainties in Periods from 1930 to 1969

“…However, uncertainties in average organ doses will be assumed to be statistically independent across the three periods; i.e., uncertain doses to individuals in different periods will be assumed to be uncorrelated. This approach to accounting for uncertainty is intended to provide unbiased estimates of risk with appropriately broad confidence intervals (e.g., Kwon et al 2016 ; Zhang et al 2017 ).…”

Methods to account for uncertainties in exposure assessment in studies of environmental exposures

“…Other methods have been developed to account for uncertainties in exposure estimation in epidemiological studies at the stage of data analysis. Zhang et al (2017) described a corrected confidence interval (CCI) approach to correct inflated variances of risk parameters estimated by the Poisson regression model due to uncertainties in the dosimetry system [57]. The CCI approximates an asymptotic distribution of parameter estimates in Poisson ERR model using multiple exposure vectors from the Monte Carlo dosimetry system.…”