International Journal of Epidemiology, Vol 26, 1071-1079, Copyright © 1997 by International Epidemiological Association
S Elmstahl and B Gullberg
BACKGROUND: If several risk factors for disease are considered in a
regression model and these factors are affected by measurement errors, the
observed relative risk will be attenuated. In nutritional epidemiology,
several nutrient variables show strong correlation, described as
collinearity. The observed relative risk will then depend not only on the
validity of the chosen diet assessment method but also on collinearity
between variables in the model. METHODS: The validity of different diet
assessment methods are compared. The correlation coefficients between
common nutrients and foods are given using data from the Malmo Food Study.
Intake of nutrients and foods were assessed with a modified diet history
method, combining a 2-week food record for beverages and lunch/dinner meals
and a food frequency questionnaire for other foods. The study population
comprised 165 men and women aged 50- 65 years. A multivariate logistic
regression model is used to illustrate the effect of collinearity on
observed relative risk (RRo). RESULTS: A moderate to high correlation
between risk factors will substantially influence RRo even when using diet
assessment methods with high validity. Methods with low validity might even
give inverse RRo. CONCLUSION: It is stressed that caution must be exercised
and only a selected number of variables should be included in the model,
especially when they are highly intercorrelated, since RRo might be
severely biased.
ARTICLES
Bias in diet assessment methods--consequences of collinearity and measurement errors on power and observed relative risks
Department of Community Medicine, Lund University, Malmo, Sweden.
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