Practice of Epidemiology Application of a Repeat-Measure Biomarker Measurement Error Model to 2 Validation Studies: Examination of the Effect of Within-Person Variation in Biomarker Measurements

Repeat-biomarker measurement error models accounting for systematic correlated within-person error can be used to estimate the correlation coefficient (q) and deattenuation factor (k), used in measurement error correction. These models account for correlated errors in the food frequency questionnaire (FFQ) and the 24-hour diet recall and random within-person variation in the biomarkers. Failure to account for within-person variation in biomarkers can exaggerate correlated errors between FFQs and 24-hour diet recalls. For 2 validation studies, q and k were calculated for total energy and protein density. In the Automated Multiple-Pass Method Validation Study (n 1⁄4 471), doubly labeled water (DLW) and urinary nitrogen (UN) were measured twice in 52 adults approximately 16 months apart (2002–2003), yielding intraclass correlation coefficients of 0.43 for energy (DLW) and 0.54 for protein density (UN/DLW). The deattenuated correlation coefficient for protein density was 0.51 for correlation between the FFQ and the 24-hour diet recall and 0.49 for correlation between the FFQ and the biomarker. Use of repeat-biomarker measurement error models resulted in a q of 0.42. These models were similarly applied to the Observing Protein and Energy Nutrition Study (1999–2000). In conclusion, within-person variation in biomarkers can be substantial, and to adequately assess the impact of correlated subject-specific error, this variation should be assessed in validation studies of FFQs.