Design and interpretation of studies of differential exposure measurement error.
Differential exposure measurement error can have more adverse effects on estimates of exposure-disease associations than nondifferential measurement error, yet relatively little has been written about the design and interpretation of validity and reliability studies to assess differential measurement error. In this paper, a simple approximate equation is given for the effect of differential measurement error in a continuous exposure measure on the bias in the odds ratio. From this, it is shown that two parameters need to be estimated in validity/reliability studies in order to interpret the results in terms of the bias in the odds ratio in an epidemiologic study that will use the measure. The first is the correlation between the mismeasured and true exposure. The second is the differential bias (difference between cases and controls in the difference between mean measured and true exposure) relative to the true difference in exposure between cases and controls. It is shown that this latter parameter can be estimated in a method comparison study if one has a comparison measure that is unbiased or has nondifferential bias, so a perfect criterion measure is not needed. Researchers should consider measuring and reporting this parameter in validity/reliability studies when feasible.
White
Emily
E
Department of Epidemiology, University of Washington, and Cancer Prevention Research Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. ewhite@fhcrc.org
D015982:Bias; D004781:Environmental Exposure; D015340:Epidemiologic Research Design; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity