Gary E Fraser1, David J Shavlik. 1. Center for Health Research, School of Public Health, Loma Linda University, Loma Linda, CA 92350, USA. gfraser@sph.llu.edu
Abstract
PURPOSE: It is unclear how well questionnaire or so-called reference methods of dietary assessment correlate with true dietary intake. We develop a method to estimate such correlations. METHODS: An error model is described that uses data from a food frequency questionnaire (Q), a reference method (R), and a biological marker (M). The model does not assume the classical error model for either R or M, or that the correlation between errors in the questionnaire and reference data is zero. Credible intervals can be placed about correlations between R, Q, M and true dietary data (T), also about the correlations between errors in reference and questionnaire data. RESULTS: Application of this model to a validation data set mainly found correlations in the range 0.4 to 0.8, and that correlations (R,T) generally exceeded correlations (Q,T), providing evidence that R is more valid than Q. Estimated correlations between errors in R and Q were often far from zero suggesting that regression calibration to imperfect reference data is problematic unless these error correlations can be estimated. CONCLUSION: A biological marker in addition to dietary data, allows calculation of correlations between estimated and true dietary intakes under reasonable assumptions about errors. However, sensitivity analyses are necessary on one variable.
PURPOSE: It is unclear how well questionnaire or so-called reference methods of dietary assessment correlate with true dietary intake. We develop a method to estimate such correlations. METHODS: An error model is described that uses data from a food frequency questionnaire (Q), a reference method (R), and a biological marker (M). The model does not assume the classical error model for either R or M, or that the correlation between errors in the questionnaire and reference data is zero. Credible intervals can be placed about correlations between R, Q, M and true dietary data (T), also about the correlations between errors in reference and questionnaire data. RESULTS: Application of this model to a validation data set mainly found correlations in the range 0.4 to 0.8, and that correlations (R,T) generally exceeded correlations (Q,T), providing evidence that R is more valid than Q. Estimated correlations between errors in R and Q were often far from zero suggesting that regression calibration to imperfect reference data is problematic unless these error correlations can be estimated. CONCLUSION: A biological marker in addition to dietary data, allows calculation of correlations between estimated and true dietary intakes under reasonable assumptions about errors. However, sensitivity analyses are necessary on one variable.
Authors: Saijuan Zhang; Douglas Midthune; Patricia M Guenther; Susan M Krebs-Smith; Victor Kipnis; Kevin W Dodd; Dennis W Buckman; Janet A Tooze; Laurence Freedman; Raymond J Carroll Journal: Ann Appl Stat Date: 2011-06-01 Impact factor: 2.083
Authors: Karen Jaceldo-Siegl; Jing Fan; Joan Sabaté; Synnove F Knutsen; Ella Haddad; W Lawrence Beeson; R Patti Herring; Terrence L Butler; Hannelore Bennett; Gary E Fraser Journal: Public Health Nutr Date: 2011-05-06 Impact factor: 4.022
Authors: Gary E Fraser; Ru Yan; Terry L Butler; Karen Jaceldo-Siegl; W Lawrence Beeson; Jacqueline Chan Journal: Epidemiology Date: 2009-03 Impact factor: 4.822
Authors: Serena Tonstad; Karen Jaceldo-Siegl; Mark Messina; Ella Haddad; Gary E Fraser Journal: Public Health Nutr Date: 2015-10-09 Impact factor: 4.022
Authors: Gary E Fraser; Karen Jaceldo-Siegl; Susanne M Henning; Jing Fan; Synnove F Knutsen; Ella H Haddad; Joan Sabaté; W Lawrence Beeson; Hannelore Bennett Journal: J Nutr Date: 2016-02-03 Impact factor: 4.798
Authors: Søren Brage; Kate Westgate; Paul W Franks; Oliver Stegle; Antony Wright; Ulf Ekelund; Nicholas J Wareham Journal: PLoS One Date: 2015-09-08 Impact factor: 3.240