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# 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.