Handling missing data in an FFQ

Multiple imputation and nutrient intake estimates

Bibliographic Data

ID15097593
AuthorsMari Ichikawa (Nagoya City University), Akihiro Hosono (0000-0002-7671-0485, Nagoya City University), Yuya Tamai (0000-0002-3228-7590, Nagoya City University), Miki Watanabe (0000-0003-0222-9629, Nagoya City University), Kiyoshi Shibata (Nagoya City University), Shoko Tsujimura (Nagoya City University), Kyoko Oka (0000-0002-1648-1596, Nagoya City University), Hitomi Fujita (0000-0003-2859-3381, Nagoya City University), Naoko Okamoto (Nagoya City University), Mayumi Kamiya (Nagoya City University), Fumi Kondo (Nagoya City University), Ryozo Wakabayashi (Nagoya City University), Tohru Noguchi (0000-0001-9165-5501, Nagoya City University), Taiji Noguchi, Tatsuya Isomura (Tokyo Medical University), Nahomi Imaeda (0000-0003-3106-3488, Shigakkan University), Chiho Goto (0000-0001-5645-8852, Nagoya Bunri University), Tamaki Yamada (0000-0002-8907-7041, Japan Medical Association), Sadao Suzuki (0000-0001-5988-4849, Nagoya City University, corresponding author)
Year2019
Volume22
Issue8
Pages1351-1360
Publication date2019-06-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePublic Health Nutrition (JOURNAL)
Journal identifiersISSN: 1368-9800 • E-ISSN: 1475-2727
PublisherCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/s1368980019000168
PMID30803461
OpenAlexW2917522185
LanguageEN
References cited23

Objective 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. Design 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. Setting A methodological sub-study of the Okazaki J-MICC study. Participants 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. Results 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. Conclusions 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

Econometrics · Imputation (statistics · Missing data · Statistics · Mathematics · Nutrition, Health and Food Behavior · Nutritional Studies and Diet · Obesity, Physical Activity, Diet