title: A potentially useful distribution model for dietary intake data.
abstract: BACKGROUND
  Conventional mixed models for the analysis of diet diary data have introduced several simplifying assumptions, such as that of a single standard deviation for within-person day-to-day variation which is common to all individuals.
  
  
  OBJECTIVE
  We developed a model in which the within-person standard deviation was allowed to differ from person to person.
  
  
  METHODS
  The model was demonstrated using data on daily retinol intake from the Dietary and Nutritional Survey of British Adults. The data were from 7-day weighed dietary diaries. Estimation was performed by Markov chain Monte Carlo. Reliability of the model was assessed from the accuracy of estimation of the percentage of days on which various intakes were exceeded. For levels above the median retinol intake, estimation of percentages of days with excessive intakes was most accurate using the model with varying within-person standard deviation.
  
  
  METHODS
  A survey of British adults aged 16-64 years.
  
  
  METHODS
  In total 2197 adults living in the UK, 1087 males and 1110 females.
  
  
  RESULTS
  Under the traditional model, estimated daily intake ranged from 716.4 to 1421.8 microg depending on age and sex, with a within-person standard deviation of 4298.9 microg. Under the new model, estimated average daily intake ranged from 388.9 to 518.3 microg depending on age and sex, but with a within-person standard deviation varying between subjects with a 95% range of 29 to 8384 microg. The new model was shown to predict the percentage of days of exceeding large intakes more successfully than the traditional model. For example, the percentage of days of exceeding the maximum recommended intake (9000 microg for men and 7500 microg for women) was 2.4%. The traditional model predicted no excessive intakes, whereas the new model predicted 2.9%.
  
  
  CONCLUSIONS
  This model is potentially useful in dietary research in general and for analysis of data on chemical contaminants in foods, in particular.
authors.affiliation: Cancer Research UK Department of Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, Charterhouse Square, EC1M 6BQ, UK. jonathan.myles@cancer.org.uk
authors.forename: J P
authors.initials: JP
authors.lastname: Myles
authors.forename: G M
authors.initials: GM
authors.lastname: Price
authors.forename: N
authors.initials: N
authors.lastname: Hunter
authors.forename: M
authors.initials: M
authors.lastname: Day
authors.forename: S W
authors.initials: SW
authors.lastname: Duffy
mesh_terms: D000293:Adolescent; D000328:Adult; D005247:Feeding Behavior; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009010:Monte Carlo Method; D009749:Nutrition Surveys; D015588:Observer Variation; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D006113:United Kingdom; D014801:Vitamin A / Q000008:administration & dosage*
chemical_list: D014801:Vitamin A