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'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake
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Missing data in FFQs: making assumptions about item non-response.

Karen E Lamb1, Dana Lee Olstad1, Cattram Nguyen2

  • 11Institute for Physical Activity and Nutrition,School of Exercise and Nutrition Sciences,Deakin University,221 Burwood Highway,Burwood,VIC 3125,Australia.

Public Health Nutrition
|December 8, 2016
PubMed
Summary

Researchers must properly handle missing data from Food Frequency Questionnaires (FFQs) to ensure accurate dietary assessments. Current methods like single imputation are flawed; multiple imputation and simulation studies are recommended for reliable nutritional epidemiology.

Keywords:
Dietary assessmentFFQImputationItem non-responseMissing data

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Area of Science:

  • Nutritional Epidemiology
  • Dietary Assessment Methods
  • Biostatistics

Background:

  • Food Frequency Questionnaires (FFQs) are widely used in epidemiological studies for dietary intake assessment.
  • Item non-response in FFQs presents challenges for researchers in deriving accurate dietary exposures.
  • Missing data handling is critical for valid interpretation of diet-health relationships.

Purpose of the Study:

  • To review current practices for addressing item non-response in FFQs.
  • To propose a research agenda for reporting and handling missing data in FFQs.
  • To highlight the assumptions and limitations of various imputation methods.

Main Methods:

  • Discussion of common single imputation techniques (zero, mean imputation).
  • Comparison with multiple imputation methods, noting their underutilization in nutritional epidemiology.
  • Emphasis on the assumptions inherent in different missing data handling approaches.

Main Results:

  • Single imputation methods can lead to biased results and incorrect inferences due to unaddressed uncertainty.
  • Multiple imputation, while increasingly used in epidemiology, is seldom applied to FFQ missing data.
  • Current practices often fail to account for the missing data mechanism, impacting study validity.

Conclusions:

  • Appropriate handling and transparent reporting of missing data in FFQ analyses are essential.
  • Simulation studies are needed to systematically evaluate imputation methods under various missing data mechanisms.
  • Adopting robust methods will improve the reliability of nutritional epidemiology research.