title: What do measures of agreement (κ) tell us about quality of exposure assessment? Theoretical analysis and numerical simulation.
abstract: BACKGROUND
  The reliability of binary exposure classification methods is routinely reported in occupational health literature because it is viewed as an important component of evaluating the trustworthiness of the exposure assessment by experts. The Kappa statistics (κ) are typically employed to assess how well raters or classification systems agree in a variety of contexts, such as identifying exposed participants in a population-based epidemiological study of risks due to occupational exposures. However, the question we are really interested in is not so much the reliability of an exposure assessment method, although this holds value in itself, but the validity of the exposure estimates. The validity of binary classifiers can be expressed as a method's sensitivity (SN) and specificity (SP), estimated from its agreement with the error-free classifier.
  
  
  RESULTS
  We describe a simulation-based method for deriving information on SN and SP that can be derived from κ and the prevalence of exposure, since an analytic solution is not possible without restrictive assumptions. This work is illustrated in the context of comparison of job-exposure matrices assessing occupational exposures to polycyclic aromatic hydrocarbons.
  
  
  CONCLUSIONS
  Our approach allows the investigators to evaluate how good their exposure-assessment methods truly are, not just how well they agree with each other, and should lead to incorporation of information of validity of expert assessment methods into formal uncertainty analyses in epidemiology.
authors.affiliation: Department of Environmental and Occupational Health, School of Public Health, Drexel University, Philadelphia, Pennsylvania, USA.
authors.forename: Igor
authors.initials: I
authors.lastname: Burstyn
authors.forename: Frank
authors.initials: F
authors.lastname: de Vocht
authors.forename: Paul
authors.initials: P
authors.lastname: Gustafson
keywords: OCCUPATIONAL & INDUSTRIAL MEDICINE; STATISTICS & RESEARCH METHODS