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

Laurence S. Freedman, Douglas Midthune, Raymond J. Carroll, Nataša Tasevska, Arthur Schatzkin, Julie Mares, Lesley Tinker, Nancy Potischman, Victor Kipnis

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1238-1245
Number of pages8
JournalAmerican journal of epidemiology
Volume174
Issue number11
DOIs
StatePublished - Dec 1 2011

Keywords

  • Bias (epidemiology)
  • Carotenoids
  • Cataract
  • Lutein
  • Measurement error
  • Sample size

ASJC Scopus subject areas

  • Epidemiology

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