title: Measurement error in epidemiology: the design of validation studies I: univariate situation.
abstract: It is becoming standard practice in epidemiology to adjust relative risk estimates to remove the bias caused by non-differential errors in the exposure measurement. Estimation of the correction factor is often based on a validation study incorporating repeated measures of exposure, which are assumed to be independent. This assumption is difficult to verify and often likely to be false. We examine the effect of departures from this assumption on the correction factor estimate, and explore the design of validation studies using two or even three different types of measurement of exposure, where assumption of independence between the measures may be more realistic. The value of good biomarker measures of exposure is demonstrated even if they are feasible to use only in a validation study.
authors.affiliation: Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong.
authors.forename: M Y
authors.initials: MY
authors.lastname: Wong
authors.forename: N E
authors.initials: NE
authors.lastname: Day
authors.forename: S A
authors.initials: SA
authors.lastname: Bashir
authors.forename: S W
authors.initials: SW
authors.lastname: Duffy
mesh_terms: D015982:Bias; D015415:Biomarkers; D004034:Diet Surveys; D004574:Electromagnetic Fields / Q000009:adverse effects; D004734:Energy Metabolism; D004813:Epidemiology / Q000706:statistics & numerical data*; D006339:Heart Rate; D006801:Humans; D009369:Neoplasms / Q000209:etiology; D012044:Regression Analysis; D012306:Risk; D011795:Surveys and Questionnaires
chemical_list: D015415:Biomarkers