title: Using regression calibration equations that combine self-reported intake and biomarker measures to obtain unbiased estimates and more powerful tests of dietary associations.
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.
authors: Biostatistics Unit, Gertner Institute for Epidemiology and Health Policy Research, Tel Hashomer, Israel. lsf@actcom.co.il
authors: Laurence S
authors: LS
authors: Freedman
authors: Douglas
authors: D
authors: Midthune
authors: Raymond J
authors: RJ
authors: Carroll
authors: Nataŝa
authors: N
authors: Tasevska
authors: Arthur
authors: A
authors: Schatzkin
authors: Julie
authors: J
authors: Mares
authors: Lesley
authors: L
authors: Tinker
authors: Nancy
authors: N
authors: Potischman
authors: Victor
authors: V
authors: Kipnis
mesh_terms: D015415:Biomarkers; D003198:Computer Simulation; D004032:Diet / Q000009:adverse effects*; D015930:Diet Records; D004194:Disease / Q000209:etiology*; D005260:Female; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis
chemical_list: D015415:Biomarkers