EXPOSURE MEASUREMENT ERRORS, RISK ESTIMATE AND STATISTICAL POWER IN CASE-CONTROL STUDIES USING DICHOTOMOUS-ANALYSIS OF A CONTINUOUS EXPOSURE VARIABLE

Background. Non-differential errors in exposure measurements have been shown to lead to differential misclassification of exposure. As a consequence, the common tenet that, in absence of bias, imprecise exposure assessment can only bias the risk estimates conservatively does not necessarily hold. We investigate the effects of exposure measurement errors on the risk estimate and on statistical power. Methods. We used a computer model that simulates a case-control study. We used both hypothetical data and data modelled on empirical measurements of environmental magnetic fields exposure. Results. Measurement errors are found to have a lesser impact on risk estimates and statistical power than would have been the case had misclassification been truly non-differential. However, for a given cutpoint, a bias away from the null cannot be excluded. The predominant direction of the errors is found to have important consequences on both the study power and the risk estimates. Conclusion. When sufficient empirical data are available, computer modelling may give a more accurate estimate of the effects of measurement errors than algebraic corrections.