Measurement error in epidemiology: the design of validation studies I: univariate situation

Stat Med. 1999 Nov 15;18(21):2815-29. doi: 10.1002/(sici)1097-0258(19991115)18:21<2815::aid-sim280>3.0.co;2-#.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bias*
  • Biomarkers
  • Diet Surveys
  • Electromagnetic Fields / adverse effects
  • Energy Metabolism
  • Epidemiology / statistics & numerical data*
  • Heart Rate
  • Humans
  • Neoplasms / etiology
  • Regression Analysis
  • Risk*
  • Surveys and Questionnaires*

Substances

  • Biomarkers