Using Regression Calibration Equations That Combine Self-Reported Intake and Biomarker Measures to Obtain Unbiased Estimates and More Powerful Tests of Dietary Associations
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The authors describe a statistical method of combining self-reports and biomarkers that, with adequate control for confounding, will provide nearly unbiased estimates of diet-disease associations and a valid test of the null hypothesis of no association. The method is based on regression calibration. In cases in which the diet-disease association is mediated by the biomarker, the association needs to be estimated as the total dietary effect in a mediation model. However, the hypothesis of no association is best tested through a marginal model that includes as the exposure the regression calibration-estimated intake but not the biomarker. The authors illustrate the method with data from the Carotenoids and Age-Related Eye Disease Study (2001--2004) and show that inclusion of the biomarker in the regression calibration-estimated intake increases the statistical power. This development sheds light on previous analyses of diet-disease associations reported in the literature.
author list (cited authors)
Freedman, L. S., Midthune, D., Carroll, R. J., Tasevska, N., Schatzkin, A., Mares, J., ... Kipnis, V.