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Summary
This summary is machine-generated.

Measurement error in dietary assessments like food frequency questionnaires (FFQ) and 24-hour recalls (24HR) can be corrected without repeated measurements. This study applies regression calibration to the EPIC-InterAct data, yielding different results than naive estimation.

Keywords:
Cox modelMeasurement errorMoment methodNutritional epidemiology

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Area of Science:

  • Nutritional epidemiology
  • Biostatistics
  • Dietary assessment methodology

Background:

  • Measurement error is a significant issue in nutritional epidemiology, particularly with dietary assessment tools like food frequency questionnaires (FFQ) and 24-hour dietary recalls (24HR).
  • The EPIC-InterAct Study lacked replicated dietary measurements, posing a challenge for standard measurement error models.
  • Developing methods to correct for measurement error in the absence of repeated data is crucial for accurate epidemiological findings.

Purpose of the Study:

  • To develop and apply a statistical method for correcting measurement error in dietary intake data from the EPIC-InterAct Study.
  • To estimate unknown parameters in a measurement error model without relying on replicated dietary measurements.
  • To assess the impact of measurement error correction on the association between diet and health outcomes.

Main Methods:

  • A moment method was employed to estimate parameters of an error model incorporating correlated errors between FFQ and 24HR measurements.
  • Correction factors and reliability ratios for nutrients were derived from the estimated parameters.
  • Regression calibration (RC) within a Cox proportional hazards model was utilized to adjust for measurement error in nutrient intake variables.

Main Results:

  • Naive estimation of dietary intakes differed substantially from estimates accounting for measurement error.
  • Regression calibration (RC) yielded adjusted hazard ratios (HR) for males: vegetable plus fruit (1.01), fat (1.30), and energy (1.16).
  • For females, the adjusted HR for energy was 0.99, contrasting significantly with naive estimates.

Conclusions:

  • It is feasible to estimate unknown parameters and correct for measurement error in dietary data without repeated measurements, using a proposed error model and specific assumptions.
  • Regression calibration (RC) provides a viable method to correct for measurement error in the EPIC-InterAct Study.
  • Correcting for measurement error is essential to prevent misleading results often produced by naive estimation methods in nutritional epidemiology.