Using regression calibration equations that combine self-reported intake and biomarker measures to obtain unbiased estimates and more powerful tests of dietary associations.

Publication Type:

Journal Article

Source:

American journal of epidemiology, Volume 174, Issue 11, p.1238-45 (2011)

Keywords:

2011, Biological Markers, Center-Authored Paper, Computer Simulation, diet, Diet Records, DISEASE, Female, Humans, Jan 12, January 2012, Models, Statistical, Public Health Sciences Division, Regression Analysis

Abstract:

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.