Missing portion sizes in FFQ – alternatives to use of standard portions
Bibliographic Data
| ID | 15095458 |
|---|---|
| Authors | Rasmus Køster-Rasmussen, Rasmus Køster‐Rasmussen (0000-0002-6452-1839, University of Southern Denmark, corresponding author), Volkert Siersma (0000-0003-1941-2681, University of Copenhagen), Thorhallur I Halldorsson, Þórhallur I Halldórsson (0000-0002-3488-0777, Statens Serum Institut), Niels de Fine Olivarius (0000-0001-6465-3615, University of Copenhagen), Jan Erik Henriksen (0000-0002-1908-7017, Odense University Hospital), Berit L Heitmann (0000-0002-6809-4504, The University of Sydney) |
| Year | 2015 |
| Volume | 18 |
| Issue | 11 |
| Pages | 1914-1921 |
| Publication date | 2015-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Public Health Nutrition (JOURNAL) |
| Journal identifiers | ISSN: 1368-9800 • E-ISSN: 1475-2727 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/s1368980014002389 |
| PMID | 25382388 |
| OpenAlex | W2122324382 |
| Language | EN |
| Citations received | 1 |
| References cited | 18 |
Objective Standard portions or substitution of missing portion sizes with medians may generate bias when quantifying the dietary intake from FFQ. The present study compared four different methods to include portion sizes in FFQ. Design We evaluated three stochastic methods for imputation of portion sizes based on information about anthropometry, sex, physical activity and age. Energy intakes computed with standard portion sizes, defined as sex-specific medians (median), or with portion sizes estimated with multinomial logistic regression (MLR), ‘comparable categories’ (Coca) or k -nearest neighbours (KNN) were compared with a reference based on self-reported portion sizes (quantified by a photographic food atlas embedded in the FFQ). Setting The Danish Health Examination Survey 2007–2008. Subjects The study included 3728 adults with complete portion size data. Results Compared with the reference, the root-mean-square errors of the mean daily total energy intake (in kJ) computed with portion sizes estimated by the four methods were (men; women): median (1118; 1061), MLR (1060; 1051), Coca (1230; 1146), KNN (1281; 1181). The equivalent biases (mean error) were (in kJ): median (579; 469), MLR (248; 178), Coca (234; 188), KNN (−340; 218). Conclusions The methods MLR and Coca provided the best agreement with the reference. The stochastic methods allowed for estimation of meaningful portion sizes by conditioning on information about physiology and they were suitable for multiple imputation. We propose to use MLR or Coca to substitute missing portion size values or when portion sizes needs to be included in FFQ without portion size data
Econometrics · Missing data · Statistics · Colorectal Cancer Screening and Detection · Environmental Science · Mathematics
Multiple imputation in health‐are databases
Dietary underreporting by obese individuals--is it specific or non-specific?
Using Intake Biomarkers to Evaluate the Extent of Dietary Misreporting in a Large Sample of Adults
Multiple imputation for missing data in epidemiological and clinical research
Multiple Imputation for Nonresponse in Surveys
Criterion-related validity of the last 7-day, short form of the International Physical Activity Questionnaire in Swedish adults
Comparing methods for handling missing values in food-frequency questionnaires and proposing k nearest neighbours imputation
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,11 |
| Citation span | 2017 - 2017 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |