title: Berkson errors in radiation dose assessments and their impact on radiation risk estimates.
abstract: OBJECTIVE
  To estimate the influence of Berkson errors in exposure doses on the results of risk analysis within example of radiation epidemiological studies of the thyroid cancer prevalence.
  
  
  METHODS
  The impact of Berkson errors of the thyroid doses in a dose-response analysis is studied by the method of stochastic simulation.
  
  
  RESULTS
  Presence of errors in doses results in bias of the naive estimations of baseline morbidity and absolute risk excess in the linear logistic regression model. With the increase of dose errors the bias of the naive estimate increases almost linearly. The use of the full maximum likelihood method developed by authors improves essentially the estimates of parameters in the excess absolute risk model.
  
  
  CONCLUSIONS
  Ignoring of the significant Berkson errors in dose assessment leads to the bias in estimates of background morbidity (overestimated) and of the excess absolute risk (underestimated). At that the bias is essentially less than the one for the case of classical error.
authors.forename: S V
authors.initials: SV
authors.lastname: Masiuk
authors.forename: S V
authors.initials: SV
authors.lastname: Shklyar
authors.forename: A G
authors.initials: AG
authors.lastname: Kukush
mesh_terms: D003198:Computer Simulation; D004781:Environmental Exposure / Q000032:analysis* / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D009381:Neoplasms, Radiation-Induced / Q000453:epidemiology*; D015995:Prevalence; D011829:Radiation Dosage; D018570:Risk Assessment; D013269:Stochastic Processes; D013964:Thyroid Neoplasms / Q000453:epidemiology* / Q000209:etiology*
keywords: Berkson error; doses of exposure; full maximum likelihood method; naive estimator; radiation risk