| Titre : | Using Regression Calibration Equations That Combine Self-Reported Intake and Biomarker Measures to Obtain Unbiased Estimates and More Powerful Tests of Dietary Associations. (2011) |
| Auteurs : | Laurence-S FREEDMAN ; Raymond-J CARROLL ; Victor KIPNIS ; Julie MARES ; Douglas MIDTHUNE ; Nancy POTISCHMAN ; Arthur SCHATZKIN ; Natasa TASEVSKA ; Lesley TINKER |
| Type de document : | Article |
| Dans : | American journal of epidemiology (vol. 174, n° 11, 2011) |
| Pagination : | 1238-1245 |
| Langues: | Anglais |
| Mots-clés : | Cataracte ; Régression ; Autoévaluation ; Estimation ; Régime alimentaire ; Alimentation ; Aliment ; Association ; Biais ; Epidémiologie |
| Résumé : | [BDSP. Notice produite par INIST-CNRS oB8R0x7C. Diffusion soumise à autorisation]. 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. |
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