Measurement error in epidemiology: the design of validation studies II: bivariate situation

✍ Scribed by M. Y. Wong; N. E. Day; N. J. Wareham

• Publisher: John Wiley and Sons
• Year: 1999
• Tongue: English
• Weight: 129 KB
• Volume: 18
• Category: Article
• ISSN: 0277-6715
• DOI: 10.1002/(sici)1097-0258(19991115)18:21<2831::aid-sim282>3.0.co;2-3

✦ Synopsis

The bias in relative risk estimates caused by errors in measurement of the relevant exposure is being increasingly recognized in epidemiology. Estimation of the necessary correction factor to remove this bias for univariate exposure has been considered in an earlier paper. We consider here the multivariate situation in which non-di!erential errors in measurement can lead to incorrect identi"cation of the variable most closely associated with disease. Estimation of the necessary correction factor when the true exposure is unobservable necessarily requires assumptions. We explore the robustness of the estimation to departures from a range of assumptions. The value of good biomarkers is demonstrated. We present a bivariate example in which failure to take account of measurement error leads to the incorrect exposure being identi"ed as the important determinant of disease risk.