HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Citation Map Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Fraser, G. E Search for Related Content PubMed PubMed Citation Articles by Fraser, G. E Agricola Articles by Fraser, G. E

A search for truth in dietary epidemiology^1,2,3,4

¹ From the Department of Epidemiology and Biostatistics, Loma Linda University, Loma Linda, CA.

Although results from epidemiologic studies of diet have taught us a great deal, much of the evidence remains mired in controversy because of the inconsistency of results among apparently good studies. I conclude that this can be largely explained by the combination of 2 problems: confounding and measurement error. This recognition allows some judgment as to which studies may be less prone to these difficulties and a search for new analytic methods that can produce less biased and more consistent results. The potential correlations between many nutrients, and to a lesser extent foods, make it difficult to know whether the nominated variable is actually the active principle or whether there is some other dietary risk factor that is closely associated. It is not generally recognized that all traditional analyses of this sort are based on a powerful but incorrect assumption: that there are no errors in dietary assessment. If the incorrect assumption is not satisfied, relative risk estimates become distorted—reduced by one-half or more in some cases. Regression calibration is a newer technique that uses a calibration substudy to provide information about errors and to correct results from the main study. There are a number of variants of this technique, all requiring assumptions about the data. Regression calibration methods that use carefully selected biological surrogates (correlates) of the dietary factor of interest in the calibration study seem to use more realistic assumptions.

Key Words: Measurement error • bias • confounding • dietary patterns • regression calibration

This article has been cited by other articles:

				M. L. Neuhouser, L. Tinker, P. A. Shaw, D. Schoeller, S. A. Bingham, L. V. Horn, S. A. A. Beresford, B. Caan, C. Thomson, S. Satterfield, et al. Use of Recovery Biomarkers to Calibrate Nutrient Consumption Self-Reports in the Women's Health Initiative Am. J. Epidemiol., May 15, 2008; 167(10): 1247 - 1259. [Abstract] [Full Text] [PDF]

				K. Jaceldo-Siegl, G. E Fraser, J. Chan, A. Franke, and J. Sabate Validation of soy protein estimates from a food-frequency questionnaire with repeated 24-h recalls and isoflavonoid excretion in overnight urine in a Western population with a wide range of soy intakes Am. J. Clinical Nutrition, May 1, 2008; 87(5): 1422 - 1427. [Abstract] [Full Text] [PDF]

				S. A. A. Beresford, L. M. Klesges, and H. R. H. Rockett Introduction J. Nutr., January 1, 2008; 138(1): 183S - 184S. [Full Text] [PDF]

				U. Nothlings, K. Hoffmann, M. M. Bergmann, and H. Boeing Fitting Portion Sizes in a Self-Administered Food Frequency Questionnaire J. Nutr., December 1, 2007; 137(12): 2781 - 2786. [Abstract] [Full Text] [PDF]

				D. C. Greenwood, J. K. Ransley, M. S. Gilthorpe, and J. E. Cade Use of Itemized Till Receipts to Adjust for Correlated Dietary Measurement Error Am. J. Epidemiol., November 15, 2006; 164(10): 1012 - 1018. [Abstract] [Full Text] [PDF]