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

✍ Scribed by M. Y. Wong; N. E. Day; S. A. Bashir; S. W. Duffy

Publisher: John Wiley and Sons
Year: 1999
Tongue: English
Weight: 124 KB
Volume: 18
Category: Article
ISSN: 0277-6715
DOI: 10.1002/(sici)1097-0258(19991115)18:21<2815::aid-sim280>3.0.co;2-#

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✦ Synopsis

It is becoming standard practice in epidemiology to adjust relative risk estimates to remove the bias caused by non-di!erential 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 di$cult to verify and often likely to be false. We examine the e!ect of departures from this assumption on the correction factor estimate, and explore the design of validation studies using two or even three di!erent 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.

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