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BMC Medical Research Methodology

, 7:41

First Online: 14 September 2007Received: 13 April 2007Accepted: 14 September 2007


BackgroundWe aimed at assessing the degree of measurement error in essential fatty acid intakes from a food frequency questionnaire and the impact of correcting for such an error on precision and bias of odds ratios in logistic models. To assess these impacts, and for illustrative purposes, alternative approaches and methods were used with the binary outcome of cognitive decline in verbal fluency.

MethodsUsing the Atherosclerosis Risk in Communities ARIC study, we conducted a sensitivity analysis. The error-prone exposure – visit 1 fatty acid intake 1987–89 – was available for 7,814 subjects 50 years or older at baseline with complete data on cognitive decline between visits 2 1990–92 and 4 1996–98. Our binary outcome of interest was clinically significant decline in verbal fluency. Point estimates and 95% confidence intervals were compared between naïve and measurement-error adjusted odds ratios of decline with every SD increase in fatty acid intake as % of energy. Two approaches were explored for adjustment: A External validation against biomarkers plasma fatty acids in cholesteryl esters and phospholipids and B Internal repeat measurements at visits 2 and 3. The main difference between the two is that Approach B makes a stronger assumption regarding lack of error correlations in the structural model. Additionally, we compared results from regression calibration RCAL to those from simulation extrapolation SIMEX. Finally, using structural equations modeling, we estimated attenuation factors associated with each dietary exposure to assess degree of measurement error in a bivariate scenario for regression calibration of logistic regression model.

Results and conclusionAttenuation factors for Approach A were smaller than B, suggesting a larger amount of measurement error in the dietary exposure. Replicate measures Approach B unlike concentration biomarkers Approach A may lead to imprecise odds ratios due to larger standard errors. Using SIMEX rather than RCAL models tends to preserve precision of odds ratios. We found in many cases that bias in naïve odds ratios was towards the null. RCAL tended to correct for a larger amount of effect bias than SIMEX, particularly for Approach A.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2288-7-41 contains supplementary material, which is available to authorized users.

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Autor: May A Beydoun - Jay S Kaufman - Joseph Ibrahim - Jessie A Satia - Gerardo Heiss


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