title: Correcting for bias in relative risk estimates due to exposure measurement error: a case study of occupational exposure to antineoplastics in pharmacists.
abstract: OBJECTIVE
  This paper describes 2 statistical methods designed to correct for bias from exposure measurement error in point and interval estimates of relative risk.
  
  
  METHODS
  The first method takes the usual point and interval estimates of the log relative risk obtained from logistic regression and corrects them for nondifferential measurement error using an exposure measurement error model estimated from validation data. The second, likelihood-based method fits an arbitrary measurement error model suitable for the data at hand and then derives the model for the outcome of interest.
  
  
  RESULTS
  Data from Valanis and colleagues' study of the health effects of antineoplastics exposure among hospital pharmacists were used to estimate the prevalence ratio of fever in the previous 3 months from this exposure. For an interdecile increase in weekly number of drugs mixed, the prevalence ratio, adjusted for confounding, changed from 1.06 to 1.17 (95% confidence interval [CI] = 1.04, 1.26) after correction for exposure measurement error.
  
  
  CONCLUSIONS
  Exposure measurement error is often an important source of bias in public health research. Methods are available to correct such biases.
authors.affiliation: Department of Epidemiology, Harvard School of Public Health, Boston, Mass. 02115, USA. stdls@channing.harvard.edu
authors.forename: D
authors.initials: D
authors.lastname: Spiegelman
authors.forename: B
authors.initials: B
authors.lastname: Valanis
mesh_terms: D000970:Antineoplastic Agents / Q000009:adverse effects*; D015982:Bias; D005334:Fever / Q000139:chemically induced; D006801:Humans; D009784:Occupational Diseases / Q000139:chemically induced; D016273:Occupational Exposure; D010595:Pharmacists; D010607:Pharmacy Service, Hospital; D012307:Risk Factors
chemical_list: D000970:Antineoplastic Agents