A search for truth in dietary epidemiology.
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.
Fraser
Gary E
GE
Department of Epidemiology and Biostatistics, Loma Linda University, Loma Linda, CA 92350, USA. gfraser@sph.llu.edu
D015982:Bias; D015986:Confounding Factors, Epidemiologic; D004032:Diet; D004812:Epidemiologic Methods; D006801:Humans; D011795:Surveys and Questionnaires