Usual Dietary Intakes: Details of the Method

Estimating Usual Intakes of Foods

If estimating usual intakes of foods (or any dietary component not consumed daily), follow these steps:

Step 1: Fit a two-part statistical model with correlated person-specific effects

Usual Intake = Probability x Amount

• Part I: Estimates the probability of consuming a food using logistic regression with a person-specific random effect (multiple or no covariates may be used).
• Part II: Specifies the consumption-day amount of a food using the 24HR data on a transformed scale (multiple or no covariates may be used).
• Part I and Part II are linked by:
  • allowing the two person-specific effects to be correlated, and
  • including the same covariates in both parts of the model.

Estimated
Model
Parameters

Step 2: Estimate final products depending on application of interest

• If evaluating covariate effects:
  • Test significance of model parameters associated with the covariates of interest for both parts of the model.
• If estimating the distribution of usual intake:
  • Estimate each individual's linear predictors for Part I and Part II of the model.
  • Generate random effects using 100 pseudo-persons for each individual.
  • Add random effects to the linear predictors and back-transform the amount estimate to original scale.
  • Estimate mean, standard deviation, and percentiles empirically.
• If estimating individual intake:
  • Estimate each individual's linear predictors for Part I and Part II of the model.
  • Evaluate a ratio of integrals, integrating over the person specific effects, using adaptive Gaussian quadrature to obtain the final estimate.

Estimating Usual Intakes of Nutrients

If estimating usual intakes of nutrients (or any dietary component consumed daily), the steps are simpler because there is no need to model probability. Therefore, a two-part model is not needed in Step 1.

Step 1: Fit a statistical model with person-specific effects

• Specify the consumption-day amount of a nutrient using the 24HR data on a transformed scale (multiple or no covariates may be used).

Estimated
Model
Parameters

Step 2: Estimate final products depending on application of interest

• If evaluating covariate effects:
  • Test significance of model parameters associated with the covariates of interest.
• If estimating the distribution of usual intake:
  • Estimate each individual's linear predictor.
  • Generate random effect using 100 pseudo-persons for each individual.
  • Add random effect to the linear predictor and back-transform the amount estimate to original scale.
  • Estimate mean, standard deviation, and percentiles empirically.
• If estimating individual intake:
  • Estimate each individual's linear predictor.
  • Evaluate a ratio of integrals, integrating over the person specific effect, using adaptive Gaussian quadrature to obtain the final estimate.