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

PLoS One. 2017 Apr 3;12(4):e0174641. doi: 10.1371/journal.pone.0174641. eCollection 2017.

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.

MeSH terms

  • Confidence Intervals*
  • Epidemiologic Studies*
  • Humans
  • Models, Theoretical*
  • Monte Carlo Method
  • Radiometry / methods*
  • Risk
  • Survival Analysis

Grants and funding

This work was conducted as part of the Joint Coordinating Committee for Radiation Effects Research, Projects 1.2b, Techa River Population Cancer Morbidity and Mortality, 2.2, Mayak Worker Cancer Mortality, and 2.4, Mayak Worker Dosimetry. It was jointly funded by the U.S. Department of Energy (U.S. DOE, grant nos. DE-HS0000091) and the Federal Medical Biological Agency (FMBA) of the Russian Federation. The funder provided support in the form of salaries for authors [ZZ, DLP, MS, BAN, MG, BM, VV, ES, AB, DOS], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘Author Contributions’ section. Hirosoft Corporation [DLP and BM] and Global Dosimetry Limited [AB] also provided support in the form of salaries but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.