We aimed to examine missing data in FFQ and to assess the effects on estimating dietary intake by comparing between multiple imputation and zero imputation.

We used data from the Okazaki Japan Multi-Institutional Collaborative Cohort (J-MICC) study. A self-administered questionnaire including an FFQ was implemented at baseline (FFQ1) and 5-year follow-up (FFQ2). Missing values in FFQ2 were replaced by corresponding FFQ1 values, multiple imputation and zero imputation.

A methodological sub-study of the Okazaki J-MICC study.

Of a total of 7585 men and women aged 35–79 years at baseline, we analysed data for 5120 participants who answered all items in FFQ1 and at least 50% of items in FFQ2.

Among 5120 participants, the proportion of missing data was 3·7%. The increasing number of missing food items in FFQ2 varied with personal characteristics. Missing food items not eaten often in FFQ2 were likely to represent zero intake in FFQ1. Most food items showed that the observed proportion of zero intake was likely to be similar to the probability that the missing value is zero intake. Compared with FFQ1 values, multiple imputation had smaller differences of total energy and nutrient estimates, except for alcohol, than zero imputation.

Our results indicate that missing values due to zero intake, namely missing not at random, in FFQ can be predicted reasonably well from observed data. Multiple imputation performed better than zero imputation for most nutrients and may be applied to FFQ data when missing is low.

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.