Measurement Error and Dietary Intake

1998 ◽  
pp. 139-145 ◽  
Author(s):  
Raymond J. Carroll ◽  
Laurence S. Freedman ◽  
Victor Kipnis
Keyword(s):  
Measurement Error ◽  
Dietary Intake

scholarly journals Combining biomarker and self-reported dietary intake data: A review of the state of the art and an exposition of concepts

2019 ◽  
Vol 29 (2) ◽  
pp. 617-635 ◽  
Author(s):  
Isobel Claire Gormley ◽  
Yuxin Bai ◽  
Lorraine Brennan
Keyword(s):  
Measurement Error ◽  
Dietary Intake ◽  
Measurement Errors ◽  
State Of The Art ◽  
Calibration Method ◽  
Simulation Studies ◽  
Dietary Biomarkers ◽  
Dietary Intake Assessment ◽  
Reported Data ◽  
Further Development

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 are 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.

People with a body mass index ⩾30 under-report their dietary intake: A systematic review

2017 ◽  
Vol 24 (14) ◽  
pp. 2042-2059 ◽  
Author(s):  
Helena Wehling ◽  
Joanne Lusher
Keyword(s):  
Systematic Review ◽  
Body Mass Index ◽  
Clinical Practice ◽  
Measurement Error ◽  
Dietary Intake ◽  
Body Mass ◽  
Selection Process ◽  
Dietary Assessment ◽  
Future Research ◽  
Multi Stage

Under-reporting of total energy intake is a common and well-known source of measurement error in dietary assessment, and evidence suggests that this bias is particularly significant in obese individuals. After a multi-stage selection process of eligible papers, this literature review appraised 34 papers detailing the accuracy of self-reported dietary intake in people with an obese body mass index (BMI ⩾ 30). The available literature to date shows that having a body mass index ⩾30 is associated with significant under-reporting of food intake. Future research should look into identifying effective techniques to reduce this bias in clinical practice.

scholarly journals Misclassification associated with measurement error in the assessment of dietary intake

2003 ◽  
Vol 6 (4) ◽  
pp. 393-399 ◽  
Author(s):  
Carl de Moor ◽  
Tom Baranowski ◽  
Karen W Cullen ◽  
Theresa Nicklas
Keyword(s):  
Measurement Error ◽  
Dietary Intake ◽  
Predictive Value ◽  
Dietary Assessment ◽  
Misclassification Error ◽  
Lower Sensitivity ◽  
Correct Classification ◽  
Eligibility Criteria ◽  
Cut Points ◽  
Nutrition Services

AbstractObjective:Dietary assessment has been used for certification to receive food supplements or other nutrition services and to provide feedback for educational purposes. The proportion of individuals correctly certified as eligible is a function of the amount of error that exists in the dietary measures and the level of dietary intake used to establish eligibility. Whether individuals are correctly counselled to increase or decrease the consumption of selected foods or nutrients is a function of the same factors. It is not clear, however, what percentage of individuals would be correctly classified under what circumstances. The objective of this study is to demonstrate the extent to which measurement error and eligibility criteria affect the accuracy of classification.Design:Hypothetical distributions of dietary intake were generated with varying degrees of measurement error. Different eligibility criteria were applied and the expected classification rates were determined using numerical methods.Setting and subjects:Simulation study.Results:Cut points of dietary intake at decreasing levels below the 50th percentile of true intake were associated with lower sensitivity and predictive value positive rates, but higher specificity and predictive value negative rates. The correct classification rates were lower when two cut points of dietary intake were used. Using a single cut point that was higher than the targeted true consumption resulted in higher sensitivity but lower predictive value positive, and lower specificity but higher predictive value negative.Conclusions:Current methods of dietary assessment may not be reliable enough to attain acceptable levels of correct classification. Policy-makers and educators must consider how much misclassification error they are willing to accept and determine whether more intensive methods are necessary.

scholarly journals Combining biomarker and food intake data: calibration equations for citrus intake

2019 ◽  
Vol 110 (4) ◽  
pp. 977-983 ◽  
Author(s):  
Silvia D'Angelo ◽  
Isobel Claire Gormley ◽  
Breige A McNulty ◽  
Anne P Nugent ◽  
Janette Walton ◽  
...  
Keyword(s):  
Measurement Error ◽  
Food Intake ◽  
Linear Regression ◽  
Dietary Intake ◽  
Simulation Study ◽  
Population Studies ◽  
Large Population ◽  
Calibration Equation ◽  
Calibration Equations ◽  
Biomarker Data

ABSTRACT Background Measurement error associated with self-reported dietary intake is a well-documented issue. Combining biomarkers of food intake and dietary intake data is a high priority. Objectives The aim of this study was to develop calibration equations for food intake, illustrated with an application for citrus intake. Further, a simulation-based framework was developed to determine the portion of biomarker data needed for stable calibration equation estimation in large population studies. Methods Calibration equations were developed using mean daily self-reported citrus intake (4-d semiweighed food diaries) and biomarker-derived intake (urinary proline betaine biomarker) data from participants (n = 565) as part of a cross-sectional study. Different functional specifications and biomarker transformations were tested to derive the optimal calibration equation specifications. The simulation study was developed using linear regression for the calibration equations. Stability in the calibration equation estimations was investigated for varying portions of biomarker and intake data “qualities.” Results With citrus intake, linear regression on nontransformed biomarker data resulted in the optimal calibration equation specifications and produced good-quality predicted intakes. The lowest mean squared error (14,354) corresponded to a linear regression model, defined with biomarker-derived estimates of intakes on the original scale. Using this model in a subpopulation without biomarker data resulted in an average mean ± SD citrus intake of 81 ± 66 g/d. The simulation study suggested that in large population studies, biomarker data on 20–30% of the subjects are required to guarantee stable estimation of calibration equations. This article is accompanied by a web application (“Bio-Intake”), which was developed to facilitate measurement error correction in self-reported mean daily citrus intake data. Conclusions Calibration equations proved to be a useful instrument to correct measurement error in self-reported food intake data. The simulation study demonstrated that the use of food intake biomarkers may be feasible and beneficial in the context of large population studies.

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