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Related Experiment Videos

Measurement error and dietary intake

R J Carroll1, L S Freedman, V Kipnis

  • 1Department of Statistics, Texas A&M University, College Station 77843, USA.

Advances in Experimental Medicine and Biology
|October 22, 1998
PubMed
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Statistical models for dietary intake and health outcomes can be affected by body mass index (BMI) biases and random error. A random effect significantly impacts results for usual nutrient intake and relative risk estimates.

Area of Science:

  • Nutritional epidemiology
  • Biomarker studies
  • Statistical modeling

Background:

  • Traditional statistical models for dietary intake may fail.
  • Biomarker studies suggest two sources of error: body mass index (BMI)-dependent systematic bias and a random error component.
  • The random error component resembles a one-way random effects model structure.

Purpose of the Study:

  • To investigate the impact of these biases on statistical analyses in nutritional epidemiology.
  • To examine the estimation of usual nutrient intake distribution.
  • To assess the correlation between nutrient intake and its instrument, and to estimate true relative risk from error-prone covariates.

Main Methods:

  • Review of statistical methods applied to dietary intake and health outcomes.

Related Experiment Videos

  • Analysis of error structures in dietary measurements, including systematic and random bias.
  • Application of models to estimate usual nutrient intake, nutrient correlations, and relative risk.

    • Main Results:

    • Systematic bias related to BMI showed minimal effect on the results.
    • An additional random effect in the variance structure significantly impacted overall findings.
    • This random effect influenced corrections for relative risk estimates and the estimation of usual nutrient intake distribution.

    Conclusions:

    • The presence of a random effect in dietary measurement error has a substantial impact.
    • Existing statistical models may require adjustments to account for this random component.
    • Further experimental research is needed to accurately estimate this crucial parameter in nutritional epidemiology.