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 twopart statistical model with correlated personspecific effects
Usual Intake = Probability x Amount
 Part I: Estimates the probability of consuming a food using logistic regression with a personspecific random effect (multiple or no covariates may be used).
 Part II: Specifies the consumptionday 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 personspecific 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 pseudopersons for each individual.
 Add random effects to the linear predictors and backtransform 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 twopart
model is not needed in Step 1.
Step 1: Fit a statistical model with personspecific effects
 Specify the consumptionday 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 pseudopersons for each individual.
 Add random effect to the linear predictor and backtransform 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.
