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

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.

published proceedings

  • Am J Epidemiol

altmetric score

  • 8

author list (cited authors)

  • Freedman, L. S., Midthune, D., Carroll, R. J., Tasevska, N., Schatzkin, A., Mares, J., ... Kipnis, V.

citation count

  • 38

complete list of authors

  • Freedman, Laurence S||Midthune, Douglas||Carroll, Raymond J||Tasevska, Nata┼Ła||Schatzkin, Arthur||Mares, Julie||Tinker, Lesley||Potischman, Nancy||Kipnis, Victor

publication date

  • November 2011