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Missing data in FFQs: making assumptions about item non-response

  • Karen E Lamb (a1) (a2), Dana Lee Olstad (a1), Cattram Nguyen (a2) (a3), Catherine Milte (a1) and Sarah A McNaughton (a1)...



FFQs are a popular method of capturing dietary information in epidemiological studies and may be used to derive dietary exposures such as nutrient intake or overall dietary patterns and diet quality. As FFQs can involve large numbers of questions, participants may fail to respond to all questions, leaving researchers to decide how to deal with missing data when deriving intake measures. The aim of the present commentary is to discuss the current practice for dealing with item non-response in FFQs and to propose a research agenda for reporting and handling missing data in FFQs.


Single imputation techniques, such as zero imputation (assuming no consumption of the item) or mean imputation, are commonly used to deal with item non-response in FFQs. However, single imputation methods make strong assumptions about the missing data mechanism and do not reflect the uncertainty created by the missing data. This can lead to incorrect inference about associations between diet and health outcomes. Although the use of multiple imputation methods in epidemiology has increased, these have seldom been used in the field of nutritional epidemiology to address missing data in FFQs. We discuss methods for dealing with item non-response in FFQs, highlighting the assumptions made under each approach.


Researchers analysing FFQs should ensure that missing data are handled appropriately and clearly report how missing data were treated in analyses. Simulation studies are required to enable systematic evaluation of the utility of various methods for handling item non-response in FFQs under different assumptions about the missing data mechanism.

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1. Willett, W (2013) Nutritional Epidemiology, 3rd ed. Oxford: Oxford University Press.
2. Subar, AF, Freedman, LS, Tooze, JA et al. (2015) Addressing current criticism regarding the value of self-report dietary data. J Nutr 145, 26392645.
3. Molag, ML, de Vries, JHM, Ocke, MC et al. (2007) Design characteristics of food frequency questionnaires in relation to their validity. Am J Epidemiol 166, 14681478.
4. Cade, JE, Burley, VJ, Warm, DL et al. (2004) Food-frequency questionnaires: a review of their design, validation and utilisation. Nutr Res Rev 17, 522.
5. Abellana Sangra, R & Farran Codina, A (2015) The identification, impact and management of missing values and outlier data in nutritional epidemiology. Nutr Hosp 31, Suppl. 3, 189195.
6. Wirfalt, E, Drake, I & Wallstrom, P (2013) What do review papers conclude about food and dietary patterns? Food Nutr Res 2013, 57.
7. Liese, AD, Krebs-Smith, SM, Subar, AF et al. (2015) The dietary patterns methods project: synthesis of findings across cohorts and relevance to dietary guidance. J Nutr 145, 393402.
8. Hansson, LM & Galanti, MR (2000) Diet-associated risks of disease and self-reported food consumption: how shall we treat partial nonresponse in a food frequency questionnaire? Nutr Cancer 36, 16.
9. Parr, CL, Hjartaker, A, Scheel, I et al. (2008) Comparing methods for handling missing values in food-frequency questionnaires and proposing k nearest neighbours imputation: effects on dietary intake in the Norwegian Women and Cancer study (NOWAC). Public Health Nutr 11, 361370.
10. Cade, J, Thompson, R, Burley, V et al. (2002) Development, validation and utilisation of food-frequency questionnaires – a review. Public Health Nutr 5, 567587.
11. Johansson, I, Hallmans, G, Wikman, A et al. (2002) Validation and calibration of food-frequency questionnaire measurements in the Northern Sweden Health and Disease cohort. Public Health Nutr 5, 487496.
12. Lioret, S, McNaughton, SA, Cameron, AJ et al. (2014) Three-year change in diet quality and associated changes in BMI among schoolchildren living in socio-economically disadvantaged neighbourhoods. Br J Nutr 112, 260268.
13. Gaard, M, Tretli, S & Loken, EB (1995) Dietary fat and the risk of breast cancer: a prospective study of 25,892 Norwegian women. Int J Cancer 63, 1317.
14. Barzi, F, Woodward, M, Marfisi, RM et al. (2006) Analysis of the benefits of a Mediterranean diet in the GISSI-Prevenzione study: a case study in imputation of missing values from repeated measurements. Eur J Epidemiol 21, 1524.
15. Schafer, JL & Graham, JW (2002) Missing data: our view of the state of the art. Psychol Methods 7, 147177.
16. Ware, JH, Harrington, D, Hunter, DJ et al. (2012) Missing data. N Engl J Med 367, 13531354.
17. Klebanoff, MA & Cole, SR (2008) Use of multiple imputation in the epidemiologic literature. Am J Epidemiol 168, 355357.
18. Rubin, DB (1987) Multiple Imputation for Non-Response in Surveys. Hoboken, NJ: John Wiley & Sons, Inc.
19. Orlich, MJ, Singh, PN, Sabate, J et al. (2013) Vegetarian dietary patterns and mortality in Adventist Health Study 2. JAMA Intern Med 173, 12301238.
20. Rizzo, NS, Sabate, J, Jaceldo-Siegl, K et al. (2011) Vegetarian dietary patterns are associated with a lower risk of metabolic syndrome: the Adventist Health Study 2. Diabetes Care 34, 12251227.
21. Collins, LM, Schafer, JL & Kam, CM (2001) A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychol Methods 6, 330351.
22. White, IR, Royston, P & Wood, AM (2011) Multiple imputation using chained equations: issues and guidance for practice. Stat Med 30, 377399.
23. Plumpton, CO, Morris, T, Hughes, DA et al. (2016) Multiple imputation of multiple multi-item scales when a full imputation model is infeasible. BMC Res Notes 9, 45.
24. Sterne, JAC, White, IR, Carlin, JB et al. (2009) Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ 338, b2393.
25. Hebert, JR, Hurley, TG, Peterson, KE et al. (2008) Social desirability trait influences on self-reported dietary measures among diverse participants in a multicenter multiple risk factor trial. J Nutr 138, issue 1, 226S234S.
26. Miller, TM, Abdel-Maksoud, MF, Crane, LA et al. (2008) Effects of social approval bias on self-reported fruit and vegetable consumption: a randomized controlled trial. Nutr J 7, 18.
27. Fraser, G & Ru, Y (2007) Guided multiple imputation of missing data – using a subsample to strengthen the missing-at-random assumption. Epidemiology 18, 246252.
28. Michels, KB & Willett, WC (2009) Self-administered semiquantitative food frequency questionnaires patterns, predictors, and interpretation of omitted items. Epidemiology 20, 295301.
29. Ahn, Y, Paik, HY & Ahn, YO (2006) Item non-responses in mailed food frequency questionnaires in a Korean male cancer cohort study. Asia Pac J Clin Nutr 15, 170177.
30. Fraser, GE, Yan, R, Butler, TL et al. (2009) Missing data in a long food frequency questionnaire: are imputed zeroes correct? Epidemiology 20, 289294.
31. Kipnis, V, Subar, AF, Midthune, D et al. (2003) Structure of dietary measurement error: results of the OPEN biomarker study. Am J Epidemiol 158, 1421.
32. Keogh, RH & White, IR (2014) A toolkit for measurement error correction, with a focus on nutritional epidemiology. Stat Med 33, 21372155.
33. Freedman, LS, Schatzkin, A, Midthune, D et al. (2011) Dealing with dietary measurement error in nutritional cohort studies. J Natl Cancer Inst 103, 10861092.
34. CONSORT Transparent Reporting of Trials (2016) Home page. (accessed April 2016).
35. STROBE Statement: Strengthening the reporting of observational studies in epidemiology (2016) Home page. (accessed April 2016).
36. STROBE-nut: An extension of the STROBE statement for nutritional epidemiology (2016) About page. (accessed April 2016).



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