  Classical approaches to assessing dietary intake are associated with
measurement error. In an effort to address inherent measurement error in
dietary self-reported data there is increased interest in the use of dietary
biomarkers as objective measures of intake. Furthermore, there is a growing
consensus of the need to combine dietary biomarker data with self-reported
data.
  A review of state of the art techniques employed when combining biomarker and
self-reported data is conducted. Two predominant methods, the calibration
method and the method of triads, emerge as relevant techniques used when
combining biomarker and self-reported data to account for measurement errors in
dietary intake assessment. Both methods crucially assume measurement error
independence. To expose and understand the performance of these methods in a
range of realistic settings, their underpinning statistical concepts are
unified and delineated, and thorough simulation studies conducted.
  Results show that violation of the methods' assumptions negatively impacts
resulting inference but that this impact is mitigated when the variation of the
biomarker around the true intake is small. Thus there is much scope for the
further development of biomarkers and models in tandem to achieve the ultimate
goal of accurately assessing dietary intake.
