NATIONAL CENTER FOR HEALTH STATISTICS

Vital and Health Statistics

 

 

 

Data Evaluation and Methods Research

Series 2, Number 178 February 2018

Estimating Usual Dietary Intake 

From National Health and Nutrition 

Examination Survey Data Using the 

National Cancer Institute Method 
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reproduced or copied without permission; citation as to source, however, is 

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Suggested citation 

Herrick KA, Rossen LM, Parsons R, Dodd KW. Estimating usual dietary intake 

from National Health and Nutrition Examination Survey data using the National 

Cancer Institute method. National Center for Health Statistics. Vital Health Stat 

2(178). 2018. 

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Names: National Center for Health Statistics (U.S.), issuing body. 

Title: Estimating usual dietary intake from National Health and Nutrition 

 Examination Survey data using the National Cancer Institute method. 

Other titles: Vital and health statistics. Series 2, Data evaluation and 

 methods research ; no. 178. | DHHS publication ; no. (PHS) 2018-1378.

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 Health Statistics, February 2018. | Series: Vital and health statistics.

 Series 2, data evaluation and methods research ; number 178 | Series: DHHS 

 pub ; number 2018-1378 | Includes bibliographical references. 

Identifiers: LCCN 2017058786| ISBN 9780840606877 (pbk.) | ISBN 0840606877 

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Subjects: | MESH: National Health and Nutrition Examination Survey (U.S.) | 

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Vital and Health Statistics
 

Series 2, Number 178 

Estimating Usual Dietary Intake 

From National Health and 

Nutrition Examination Survey 

Data Using the National Cancer 

Institute Method 

Data Evaluation and Methods Research 

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES 

Centers for Disease Control and Prevention 

National Center for Health Statistics 

Hyattsville, Maryland 

February 2018 

DHHS Publication No. 2018–1378 
National Center for Health Statistics 

Charles J. Rothwell, M.S., M.B.A., Director 

Jennifer H. Madans, Ph.D., Associate Director for Science 

Division of Health and Nutrition Examination Surveys 

Kathryn S. Porter, M.D., M.S., Director 

Ryne Paulose-Ram, Ph.D., Associate Director for Science 

Division of Vital Statistics 

Delton Atkinson, M.P.H., M.P.H., P.M.P., Director 

Hanyu Ni, Ph.D., M.P.H., Associate Director for Science 
 

 

 

 

 

 

 

 

iii

Contents

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .v

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

NHANES Dietary Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Methods to Estimate Usual Dietary Intake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

Analytic Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

Covariates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

Dietary Components and Nutrients. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

Implementing NCI Method With NHANES Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Appendix I. Nutrient Reference Values Used in This Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Appendix II. Steps for Creating Poststratified Balanced Repeated Replication Weights . . . . . . . . . . . . . . . . . . . . . . . 29

Appendix III. SAS Syntax Used to Create Poststratified Adjusted Balanced Repeated Replication Weights for 

Use in Usual Intake Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Appendix IV. National Cancer Institute Macros Used to Estimate Usual Intake . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Appendix V. Usual Intake Estimation—Univariate–Calcium Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Appendix VI. Usual Intake Estimation—Ratio and Bivariate–Percentage of Energy From Fat Example . . . . . . . . . . . . . . 43

Appendix VII. Empirical Approach to Selecting a Correlated or Uncorrelated Model . . . . . . . . . . . . . . . . . . . . . . . . 54

Text Figures

1. Estimated distributions of calcium using three different methods for men aged 40–59: National Health and 

Nutrition Examination Survey, 2011–2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

2 Percentage of U.S. population with calcium intake below estimated average requirement using different 

estimation methods, by age and sex: National Health and Nutrition Examination Survey, 2011–2014 . . . . . . . . . . . 10

3 Estimated distributions of fruit intake using three different methods for girls aged 2–5 years: 

National Health and Nutrition Examination Survey, 2009–2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

4 Percentage of U.S. population with fruit intake below the lower recommended range, by age and sex: 

National Health and Nutrition Examination Survey, 2009–2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

5 The relationship between bias and precision when comparing given day with usual intake methods for 

estimating intake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Text Tables

A. Long-format data structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

B. Wide-format data structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

Detailed Tables

1. SAS macros developed by National Cancer Institute to run nonlinear mixed models used in this report . . . . . . . . . . 18

2. Calcium (grams per day): Mean, percentile, and percentage of persons meeting dietary reference intakes, 

calculated by given day, within-person mean, and the National Cancer Institute Method in the U.S. 

population aged 2 years and over: National Health and Nutrition Examination Survey, 2011–2014 . . . . . . . . . . . . . 19

3. Total fat (grams per day): Mean and percentile, calculated by given day, within-person mean, and the 

National Cancer Institute Method in the U.S. population aged 2 years and over: National Health and 

Nutrition Examination Survey, 2011–2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4. Energy (kilocalories per day): Mean and percentile, calculated by given day, within-person mean, and 

the National Cancer Institute Method in the U.S. population aged 2 years and over: National Health and 

Nutrition Examination Survey, 2011–2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 

5. Percentage of energy intake from total fat (per day): Mean, percentile, and percentage of persons meeting 

dietary reference intakes, calculated by given day, within-person mean, and the National Cancer Institute 

Method in the U.S. population aged 2 years and over: National Health and Nutrition Examination Survey, 

2011–2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 

6. Total fruit (cup equivalents per day): Mean, percentile, and the percentage of persons below and above the 

2015–2020 Dietary Guidelines recommended consumption range, calculated by given day, within-person 

mean, and the National Cancer Institute Method in the U.S. population aged 2 years and over: National 

Health and Nutrition Examination Survey, 2009–2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 

Appendix Tables 

I. Dietary reference intake for calcium (estimated average requirement and tolerable upper limit), percentange 

of energy from fat (acceptable macronutrient distribution range), and recommended intake range of fruit, by 

the Institute of Medicine Life Stage Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 

II. National Cancer Institute macros used to estimate usual intake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 

iv 
Acknowledgments 

The authors of this report gratefully 

acknowledge the assistance of Naman 

Ahluwalia, Amy Branum, Ryne 

Paulose-Ram, and Cynthia Ogden in the 

preparation of this report. 

v 
Abstract 

Background 

Dietary recommendations are 

intended to be met based on dietary 

intake over long periods, as 

associations between diet and health 

result from habitual intake, not a 

single eating occasion or day of 

intake. Measuring usual intake directly 

is impractical for large populationbased surveys due to the respondent 

burden associated with reporting 

habitual intake over longer periods. 

Therefore, analytical techniques were 

developed to estimate usual intake 

using as few as 2 days of 24-hour 

dietary recall data. With National 

Health and Nutrition Examination 

Survey (NHANES) data, this report 

demonstrates how to estimate usual 

intake using the National Cancer 

Institute (NCI) Method. 

Methods 

This report demonstrates how to 

estimate the usual intake of nutrients 

consumed daily or episodically using 

NHANES data. Means, percentiles, 

and the percentages above or below 

specified Dietary Reference Intake 

(DRI) values for given day, withinperson mean (WPM), and estimates 

of usual intake are presented. 

Results 

Consistent with previous analyses, 

mean intakes were similar across 

methods. However, the distributions 

estimated by nonusual intake 

methods were wider compared with 

the NCI Method, which can lead to 

misclassification of the percentage of 

the population above or below certain 

DRIs. 

Conclusion 

Use of NHANES data to examine 

the proportion of the population 

at risk of insufficiency or excess 

of certain nutrients, with methods 

like given day and WPM that do not 

address within-person variation, may 

lead to biased estimates. 

Keywords: diet • statistical 

distributions • National Health and 

Nutrition Examination Survey 

Estimating Usual Dietary Intake

From National Health and 

Nutrition Examination Survey

Data Using the National Cancer

Institute Method 

by Kirsten A. Herrick, Ph.D., M.Sc., and Lauren M. Rossen, Ph.D., 

M.S., National Center for Health Statistics; Ruth Parsons, B.A., 

Information Management Services, Inc.; and Kevin W. Dodd, Ph.D., 

M.S., National Cancer Institute 

Introduction 

The National Health and Nutrition 

Examination Survey (NHANES) is a 

program of studies that has collected 

dietary information from respondents 

since the 1970s (1). NHANES, which 

began collecting data continuously 

in 1999, is a stratified, multistage 

probability survey of the U.S. civilian 

noninstitutionalized population (2). 

NHANES data are publicly released by 

the National Center for Health Statistics 

(NCHS) every 2 years on approximately 

10,000 individuals. NHANES 

participants are administered a series 

of questionnaires in a detailed in-home 

interview, followed by a standardized 

physical examination and dietary intake 

interview at a specially designed mobile 

examination center (MEC). 

Dietary intake data from NHANES 

are routinely used to inform and evaluate 

policy. For example, NHANES data 

have been used to evaluate the adequacy 

of food packages provided by the 

Special Supplemental Nutrition Program 

for Women, Infants, and Children 

(WIC) (3). The Dietary Guidelines for 

Americans also rely on NHANES data 

to document consumption patterns in the 

United States, and the Food and Drug 

Administration uses information from 

NHANES to determine whether changes 

to the vitamin and mineral fortification 

regulations are needed (1). 

NHANES Dietary Data 

Collection 

NHANES has collected dietary data 

with two instruments—a food frequency 

questionnaire (FFQ) and a 24-hour recall. 

Since 1999, the 24-hour recall has been 

the primary source of dietary intake data 

for NHANES. Since 2002, NHANES 

has administered two 24-hour recalls 

to MEC participants: the first recall is 

administered in the MEC, and then a 

second recall is administered by phone 

3–10 days later. 

NHANES is designed so that all of 

the days of the week and seasons of the 

year are represented in the sample. This 

feature ensures that a single 24-hour 

recall can be used to estimate the average 

consumption of a population or subgroup 

(4–8) under two key assumptions. The 

first assumption is that the 24-hour 

recall provides an unbiased measure of 

true intake on a population basis (i.e., 

free from systematic error [9,10]). The 

second assumption is that seasons of the 

year and days of the week are adequately 

represented across the population. 

However, a single 24-hour recall, 

commonly referred to as “given day,” is 

an imprecise measure of an individual’s 

usual intake due in large part to 

day-to-day variation in individual diets. 

Page 1 
Page 2  Series 2, No. 178 

An important consideration 

regarding measuring dietary intake is 

minimization of measurement error. All 

self-report measures of dietary intake 

are subject to systematic and random 

measurement error (1,11–14). Since 

24-hour recalls are limited to foods 

consumed in the previous day, the 

magnitude of systematic errors related 

to recall bias is reduced compared with 

FFQs (9). However, dietary reporting 

from 24-hour recalls is impacted by the 

combination of day-to-day variation and 

variation due to other random errors, 

generally considered together as “withinperson variation” (9). 

One goal of measuring dietary intake 

is to capture usual or habitual intake or 

the dietary intake of a person over long 

periods of time (4,6). This is important 

because dietary recommendations 

are intended to be met over time, and 

diet–health relationships are often the 

product of long-term exposures, not 

a single eating occasion (15). For this 

reason, comparing nutrients from a single 

day with Dietary Reference Intakes 

(DRIs) is not recommended to evaluate 

dietary adequacy. However, given that 

most large-scale population-based 

surveys, like NHANES, use only 1 or 2 

days of 24-hour recalls as the primary 

source to measure dietary intake, more 

sophisticated analytic methods, employed 

post-data collection, are necessary to 

describe usual intake using NHANES 

dietary data (16–19). 

Methods to Estimate Usual 

Dietary Intake 

Different analytic methods for 

estimating usual intake have been 

developed over the past 3 decades 

to address the issue of within-person 

variation found in dietary intake data 

as assessed by 24-hour recalls. They 

include within-person mean, the National 

Research Council/Institute of Medicine 

(NRC/IOM) methods, the Iowa State 

University (ISU) method, the Statistical 

Program to Assess Dietary Exposure 

(SPADE), and the National Cancer 

Institute (NCI) Method (5,9,20–25), all of 

which are described briefly below. 

The simplest method for estimating 

usual intake is to average multiple 

24-hour recalls per individual and 

call this within-person mean (WPM) 

an approximation of usual intake. In 

the context of NHANES, this means 

averaging data from both days of 

dietary intake to estimate usual intake. 

This can lead to biased estimates of 

inadequate or excess intake because 

it does not: (a) distinguish withinperson from between-person variation; 

(b) account for consumption amounts 

that are at the extremes, either zero or 

extremely positively skewed; (c) allow 

for adjustment by covariates; or (d) allow 

for the correlation between the amount 

consumed and the probability to consume 

(9). Theoretically, with a larger number 

of recalls to average across, it would be 

possible to mitigate points a, b, and d. 

However, since NHANES only collects 

two recalls, use of WPM to address these 

issues is limited. 

The NRC/IOM methods represent 

an innovation in estimating usual intake, 

because within-person variation was 

recognized and partitioned for the first 

time (9). The ISU method, implemented 

by using PC-Side, software for intake 

distribution estimation developed by 

ISU (26), accounts for within-individual 

variation and can be used in complex 

surveys (27). However, if many 

24-hour recalls have no consumption 

of a food item, it cannot account for the 

large number of zero consumers, and 

adjustment by covariates is limited (5). 

The NCI Method (5) has evolved 

from earlier methods, including the ISU 

method and NRC/IOM methods, and has 

been extended for use in complex 

surveys, such as NHANES. This method 

can incorporate covariates and therefore 

provides estimates of usual intake for 

subpopulations (28), handles situations 

where the amount consumed and 

probability to consume are correlated (5), 

deals with foods or nutrients where a 

significant proportion of the population 

reports no consumption of the item of 

interest on a given day (29,30), and 

models usual intake in relation to health 

outcomes (23). SPADE is comparable to 

the NCI Method, except it can be 

implemented using the statistical software 

R as a package called SPADE.RIVM 

(25,31). 

The goal of this report is to 

demonstrate how to estimate usual 

intake appropriately by adapting one 

particular method that can be used with 

complex surveys, the NCI Method, using 

NHANES dietary data by providing 

examples of estimating usual intake of 

various nutrients and foods. Therefore, 

the focus of this report is not to describe 

dietary intake for the U.S. population 

but to supplement NCI’s User’s Guide 

(32), NHANES web tutorials (33), and 

examples from NCI’s website (34–36). 

This is done by providing specific 

examples of using usual intake methods 

with NHANES data, including some of 

the technical issues that can potentially 

arise when using the NCI Method. 

Estimates of intake, calculated by 

the NCI Method, are compared with 

intake estimates from simpler methods, 

(WPM and given day) for illustrative 

purposes. Additionally, this report 

provides an overview of the analytic 

and programming considerations that an 

analyst may encounter when applying the 

NCI Method to NHANES dietary data, 

such as how to create and use balanced 

repeated replication (BRR) weights to 

account for the complex survey design. 

Syntax and instructions are provided to 

estimate usual intake, standard error, and 

the proportion above or below a threshold 

using available NCI SAS macros (34). 

Methods 

Analytic Sample 

All participants who took part 

in the examination component of 

NHANES were eligible for the dietary 

interview. This analysis was limited to 

all respondents aged 2 years and over 

who completed at least one dietary recall 

that was reliable and met the minimum 

criteria of completeness, namely that 

four of the five steps in the Automated 

Multiple-Pass Method (AMPM) were 

completed and that foods and beverages 

consumed were identified (37,38). This 

analysis used data from the 2011–2014 

NHANES to estimate the usual intake of 
Series 2, No. 178  Page 3 

selected nutrients (i.e., calcium, total fat, 

total energy, and percentage of energy 

from fat). Data from the 2009–2012 

survey were used to estimate the usual 

intake of one type of food (i.e., fruits), 

because the Food Patterns Equivalents 

Database (FPED) for 2013–2014, which 

is used to quantify total fruit consumption 

(39), was not available for use at the time 

of this analysis. The unweighted total 

examination response rate in 2009–2014 

for participants aged 2 years and over 

ranged from 68.5% to 76.0% (40). The 

NHANES protocol was approved by 

the NCHS Research Ethics Review 

Board. Informed consent was obtained 

from persons aged 18 and over. Written 

parental consent and child assent was 

obtained for participants aged 2–17. 

Of the 18,259 respondents aged 

2 years and over examined in the 

2009–2012 NHANES, 1,131 persons did 

not provide a dietary recall, 142 supplied 

a recall but were deemed unreliable, and 

11 reported consuming some breast milk 

and were therefore excluded. The amount 

of breast milk consumed is not quantified 

in the 24-hour recall, therefore, these 

records contain missing values for the 

amount consumed and for the amount of 

energy and nutrients from breast milk. 

Consequently, no total nutrient intakes 

(contained in the Total Nutrient Intakes 

files) are available for respondents who 

reported consuming any breast milk. An 

additional 168 women who were either 

breastfeeding or pregnant were excluded, 

because the eating patterns for this 

group can differ from the general public, 

and they have separate DRIs for many 

nutrients. 

Of the 17,912 respondents aged 2 

years and over examined in the 2011– 

2014 NHANES, 1,700 persons did not 

provide a dietary recall, 200 supplied a 

recall but were deemed unreliable, and 

12 respondents reported consuming 

breast milk and were therefore excluded. 

An additional 170 women who were 

either breastfeeding or pregnant were 

excluded. The final analytic samples, 

after exclusions, comprised 16,807 

respondents in the 2009–2012 survey and 

15,830 respondents in the 2011–2014 

survey. 

Covariates 

The NCI Method allows for 

adjustment of dietary intake by 

covariates. Moreover, covariates are used 

to create the BRR weights necessary 

to use the NCI Method with NHANES 

data, and covariates also guide the 

presentation of results. The tables in this 

report present age groups at the time of 

interview (i.e., 2–5, 6–11, 12–19, 20–39, 

40–59, and 60 and over), consistent with 

NHANES sample-selection methods 

(41). Self-reported race and Hispanic 

origin was categorized as non-Hispanic 

white, non-Hispanic black, non-Hispanic 

Asian, Hispanic, and other (includes 

multiracial groups) when creating BRR 

weights for the 2011–2014 NHANES. 

Self-reported race and Hispanic origin 

was categorized as non-Hispanic white, 

non-Hispanic black, Hispanic, and other 

(includes multiracial groups) when 

creating BRR weights for the 2009–2012 

NHANES. Usual intake estimation with 

BRR weights accounts for day of the 

week, nonresponse, noncoverage, and 

unequal probabilities of selection and the 

complex sample design of NHANES. 

More details on BRR weights is provided 

in the Methods section and in Appendices 

II and III. 

Dietary Components and 

Nutrients 

During the NHANES dietary 

interview, trained interviewers, using 

the computer-assisted dietary interview 

system AMPM, collect the type and 

quantity of all foods and beverages 

consumed in a single 24-hour period, 

specifically the 24-hour period before 

the dietary interview (from midnight 

to midnight), at the MEC. AMPM 

includes standard probes to reduce the 

probability of missed or forgotten food 

items in a multiple-pass method designed 

to maximize complete and accurate 

reporting (38). A second telephoneadministered 24-hour recall is collected 

(3–10 days after the first 24-hour recall 

at the MEC). Proxies, 92% of whom 

were the participant’s mother, report 

dietary intake for children aged 5 years 

and under and assist with the dietary 

interview for children aged 6–11. Dietary 

intake is self-reported for participants 

aged 12 and over (37). 

Four nutrients and one food were 

chosen for the examples in this report, 

because they are commonly represented 

in nutritional epidemiological analyses 

and demonstrate a variety of analyses 

that can be accomplished with the NCI 

Method to estimate usual intake with 

NHANES data. These examples also 

supplement the examples provided on 

NCI’s website (34–36). These nutrients 

are calcium (grams), energy (kilocalorie), 

fat (grams), percentage of energy from 

fat, and fruit (cup equivalents). The first 

four represent ubiquitously consumed 

nutrients (i.e., nutrients that are consumed 

by nearly everyone, every day), and fruit 

represents a food that is episodically 

consumed (i.e., occasionally consumed 

by a smaller segment of the overall 

population). Approximately 25% of the 

population reported no consumption of 

fruit on both 24-hour recall days. The 

percentage of energy from fat represents 

a special type of ubiquitously consumed 

nutrient, because it is a ratio of means, 

total fat consumed divided by total 

energy consumed. Different methods are 

required for estimating the usual intake of 

ubiquitously and episodically consumed 

nutrients, foods, and ratios of nutrients, 

which are described below. Estimates of 

energy intake are presented because they 

are a component used to calculate energy 

from total fat (13,14). 

Day 1 (37,42) and Day 2 (43,44) 

Total Nutrients files from the 2011–2014 

NHANES were used for the analysis of 

calcium, energy, fat, and the percentage 

of energy from fat described in this 

report. These are summary files that 

describe the total energy and nutrients 

from the foods and beverages captured 

in the Individual Food files. For the 

estimation of the cup equivalents of 

fruit consumed, the 2009–2012 FPED 

files were used (available from the 

United States Department of Agriculture 

(USDA) website [45]). The FPED 

files use the Individual Food files and 

disaggregate each reported food and 

beverage into its component parts, 

which are then organized into 37 groups 

based on the USDA’s Food Patterns 
Page 4  Series 2, No. 178 

(FP), developed from the 2010 Dietary 

Guidelines for Americans. One FP of 

interest is the consumption of fruit, which 

is analyzed in this report. 

Dietary Reference Intakes 

The estimated population 

distribution of usual intake can be 

used to assess dietary intake relative 

to population-based recommendations 

more appropriately than WPM or 

given-day methods. To demonstrate this, 

estimated usual nutrient intakes were 

compared with sex and age-specific 

DRIs established by IOM (17,46–48). 

The DRIs presented here include the 

Estimated Average Requirements (EARs) 

and Tolerable Upper Intake Level (UL). 

EAR is “the average daily intake level 

estimated to meet the requirement of 

half of the healthy individuals in a 

particular life stage and gender group” 

and is considered the best measure of 

population adequacy of nutrient intake 

(46). Intakes lower than EAR indicate 

the estimated prevalence of inadequate 

intakes within a group. The proportion of 

the population with intakes greater than 

UL identifies those with excessive intakes 

who are potentially at risk of adverse 

effects (19). 

In contrast to DRIs, which 

address single nutrients, Acceptable 

Macronutrient Distribution Ranges 

(AMDRs) have been established for the 

energy derived from macronutrients, 

such as the percentage of energy from 

fat. Ranges are used, rather than a 

discrete number, because energy needs 

vary by individual. AMDR is the range 

of intake for a particular energy source 

that is associated with reduced risk of 

chronic disease while providing intakes 

of essential nutrients (17). Consumption 

in excess of the upper range of AMDR is 

potentially associated with increased risk 

for some diet-related chronic diseases, 

while consumption below the lower range 

of AMDR may be related to insufficient 

intakes of essential nutrients (49). 

There are no DRIs for fruit, however, 

the 2015–2020 Dietary Guidelines for 

Americans promote recommended ranges 

of intake by age and sex that can be used 

like DRIs (15). For fruit intake, this 

report presents the estimated percentages 

below the lower range and above the 

upper range of consumption. Appendix I 

provides details of the IOM DRIs by age 

and sex that are used in this analysis. 

Importantly, DRIs are meant to be 

applied to true usual intakes for a given 

population. Given day and WPM do not 

meet that standard, however, they are 

presented for illustrative purposes 

(16–19). Additionally, in the analysis 

presented in this report, DRIs were 

applied to estimates of usual intake 

distributions, generated by the NCI 

Method under the assumption that 

24-hour recalls are an unbiased measure 

of true population intake. 

Implementing NCI Method 

With NHANES Data 

The NCI Method uses mixed effects 

models, which contain both random and 

fixed effects, to estimate usual intake 

by separating and removing the withinperson variation from the between-person 

variation. For episodically consumed 

foods or nutrients, this method involves 

two-part models. The first part uses 

logistic mixed-effects models to estimate 

the probability (ranging from 0 to 1) to 

consume a food or nutrient. The second 

part uses linear mixed-effects models 

to estimate the amount of the food or 

nutrient consumed. The two parts are 

linked through the inclusion of personspecific random effects and by the 

optional inclusion of shared covariates 

(5). For ubiquitously consumed foods or 

nutrients (e.g., those consumed by nearly 

everyone in the sample), the NCI Method 

involves only the “amount” model or 

the linear mixed-effects model because 

the probability to consume is 1 or nearly 

close to 1. Additional steps are required 

beyond the two-part model to estimate 

distributions of usual intake using 

Markov chain Monte Carlo (MCMC) 

simulations, which are described below. 

There are three major components 

necessary to run the NCI usual intake 

models using NHANES data: 

1.	 Multiple 24-hour dietary recalls on a 

proportion of the population 

2.	 Poststratified, integerized BRR 

weights 

3.	 SAS macros developed by NCI to 

run nonlinear mixed models or the 

NCI Method (34) (Table 1) 

Number of 24-hour recalls 

To utilize the NCI Method, there 

must be multiple 24-hour dietary recalls 

on a proportion of the population. 

A minimum of two dietary recalls is 

necessary to separate the within-person 

from between-person variation. However, 

the sample size for analyses using the 

NCI Method is determined by the total 

number of respondents who have at 

least 1 day of dietary recall. When using 

NHANES data, this means the total 

number of respondents in the analytic 

sample will be equal to the number 

with a Day 1 recall. There is no rule 

for the proportion of the population 

with more than one recall needed to 

use the NCI Method. However, the 

variability in the nutrient is linked to 

the proportion of the population with 

duplicate recalls needed to produce 

stable and reliable estimates: the more 

variability, the greater the proportion 

of the population with duplicate recalls 

needed. Typically, approximately 85% 

of respondents in NHANES have two 

dietary recalls (37,42,50). For nutrients 

and foods consumed by a majority of the 

population every day, 85% of respondents 

with duplicate records is sufficient to 

implement the NCI Method. 

●		 The structure of the two Total 

Nutrient Intakes files (Days 1 and 2) 

of dietary recall data must be in long 

format. This means that each day of a 

person’s dietary recall is represented 

by a separate row of data, and a 

variable on the file indicates the 

day. For NHANES data, each 

participant’s identification number 

(i.e., SEQN) would thus be listed 

twice if that participant completed 

2 days of dietary recall; the first 

row represents recall data from Day 

1 and the second row represents 

recall data from Day 2 (Table A). 

The contrasting format would be a 

wide file, where the Day 2 dietary 

data would be added as additional 

variables to the individual-level 

record. SEQN would only appear 
Series 2, No. 178  Page 5 

once in the file, and dietary data 

would be represented in separate 

columns (Table B). 

Balanced repeated replication 

To analyze NHANES data 

appropriately, the differential weighting 

of individuals and the correlation of 

individuals within primary sampling 

units (PSU) and strata must be taken into 

account. Given-day intake or WPMs can 

be estimated using standard SUDAAN or 

SAS SURVEY commands, which include 

the design variables (PSU, STRATA) and 

the dietary sample weights provided by 

NHANES. However, the NCI Method 

uses the SAS procedure, NLMIXED, 

to estimate a nonlinear mixed-effects 

model. NLMIXED allows for sample 

weights but does not explicitly account 

for the complex survey design (PSU 

and STRATA). In order to account for 

both the differential weighting and the 

complex survey design of NHANES, 

BRR weights must be used with the 

NLMIXED procedure. The NLMIXED 

procedure accounts for differential 

weighting by using the REPLICATE 

statement. 

BRR is a statistical method for 

estimating the sampling variability of a 

statistic, taking into account complex 

sample design through replicate weights. 

Instead of a single weight per individual 

(as is used in Taylor series linearization 

and the standard NHANES weights), 

BRR uses multiple weights or replicates 

for each individual in the survey. BRR 

weights are derived from the original 

sample weight and the design variables 

PSU and STRATA. Appendix II describes 

the procedure to create BRR weights, 

while Appendix III provides syntax for the 

SAS procedure, PROC SURVEYFREQ, 

which was used to create the BRR 

weights described in this report. In order 

to calculate BRR weights, users must 

specify the number of BRR replicate 

weights needed and the perturbation 

factor. 

In BRR, one-half of the sample is 

used at a time: one of two PSUs from 

each stratum. In standard BRR, the 

sample weights of the observations in 

the selected PSUs are doubled, while the 

sample weights of the observations in 

Table A. Long-format data structure 

SEQN Day Nutrient_1 Nutrient_2 

1 

1 

2 

2 

1 

2 

1 

2 

500 grams 

520 grams 

870 grams 

820 grams 

10 grams 

15 grams 

2 grams 

7 grams 

Table B. Wide-format data structure 

SEQN Nutrient_1_Day1 Nutrient_1_Day2 Nutrient_2_Day1 Nutrient_2_Day2 

1 500 grams 520 grams 10 grams 15 grams 

2 870 grams 820 grams 2 grams 7 grams 

the PSU not chosen are set to zero. The 

number of replicates (R) that are needed 

for BRR is the smallest integer that is 

divisible by 4 and is greater than or equal 

to S, where S is the number of strata. 

NHANES typically has 15 strata (S) per 

survey cycle. Consequently, for 2 years of 

NHANES data, this number is 16. There 

is additionally always a base run, so the 

total number of BRR weights created are 

the numbers below, plus 1. 

●		 4 years of NHANES = 32 BRR 

weights 

○		 4 years = 15 + 15 strata = 30, 

R > 30 and divisible by 4, 

therefore 32 is the smallest 

integer 

●		 6 years of NHANES = 48 BRR 

weights 

●		 8 years of NHANES = 60 BRR 

weights 

●		 10 years of NHANES = 76 BRR 

weights 

●		 12 years of NHANES = 92 BRR 

weights 

A modification of BRR, called 

Fay’s method, is typically used when 

implementing the NCI Method with 

NHANES data. With Fay’s modification, 

the sample weights from BRR are not 

zero-weighted in one-half of the sample 

and double-weighted in the other half. 

Instead, they are weighted by a factor F 

(F is a proportion that ranges between 0 

and 1). This factor weighs down one-half 

of the sample by F, and the other half is 

weighted up by 1 – F. For example, when 

F = 0.3, the corresponding perturbation 

factor is 70%. Weights are decreased by 

30% in the one-half sample and increased 

by 70% in the other one-half sample. This 

is the standard perturbation factor used in 

other reports and analyses of NHANES 

dietary intake data (51,52). 

Special consideration related to BRR 

weights 

Prior to creating BRR weights, 

analysts must ensure that there are only 

two strata per PSU, as there are occasions 

when NHANES has three PSUs in a 

single stratum. All participants within 

one randomly selected PSU should be 

reassigned into one of the remaining two 

PSUs in that stratum. The whole PSU 

is reassigned at one time, rather than 

randomly assigning individuals in the 

third PSU to PSU 1 or 2. Sample code 

for this reassignment and recoding of the 

PSUs is shown in Appendix III. 

BRR weights created using the 

original NHANES dietary sample weight 

and survey design information will not 

necessarily reflect the same population 

totals as the original sample weights. 

This is because one-half of the PSU is 

up-weighted and the other half is downweighted. Therefore, the BRR weights 

are no longer necessarily representative 

of the U.S. population. This can be 

observed by looking at the weighted 

population totals of all combinations 

of the common domains used to create 

the representative sampling frame (i.e., 

age, sex, race and Hispanic origin, 

and comparing BRR weights with the 

original sample weight [referred to as 

control] frequencies [syntax provided in 

Appendix III demonstrates how to obtain 
Page 6  Series 2, No. 178 

control frequencies]). It is necessary to 

poststratify the BRR weights so that each 

of the replicate weights will produce 

estimates that are representative of the 

U.S. population, consistent with the 

original sample weight. In other words, 

the age-sex-race and Hispanic-originspecific population totals produced by 

the original sample weight should be 

matched by each of the BRR weights. 

It is an additional requirement of the 

macros used in the NCI Method that the 

BRR weights be integerized (i.e., rounded 

to the nearest whole number). 

For the purposes of this analysis, 

two sets of BRR weights were generated. 

For the estimation of nutrients for the 

2011–2014 NHANES (calcium, energy, 

total fat, and the percentage of energy 

from fat), all possible combinations of 

poststratification cells by age, sex, and 

race and Hispanic origin were created. 

The following categories were used for 

race and Hispanic origin: non-Hispanic 

white, non-Hispanic black, non-Hispanic 

Asian, Hispanic, and other combined, 

and for age groups: 2–5, 6–11, 12–19, 

20–30, 40–59, and 60 years and over. 

For the estimation of the usual intake 

of fruit, all possible combinations of 

poststratification cells by sex, race and 

Hispanic origin, and age were created. 

The following categories were used for 

race: non-Hispanic white, non-Hispanic 

black, Hispanic, and other combined, 

and for the age groups: 1–3, 4–8, 9–13, 

14–18, 19–30, 31–50, 50–69, and 70 

years and over. The race and Hispanicorigin categories reflect the categories 

used to create a representative sample 

in NHANES for their respective survey 

cycles. The 2011–2014 age categories 

reflect the traditional sampling and 

reporting categories for NHANES, 

while the age categories for 2009–2012 

reflect the Life Stage Groups used by 

the National Academies of Science, 

Engineering, and Medicine (formerly 

the Institute of Medicine) for DRIs 

(Appendix I). Different age groups were 

used to examine the ease of implementing 

the NCI Method with each of the age 

categorizations. Analysts are encouraged 

to choose the age categorization that best 

matches the goals of their analysis. 

Appendix II details the steps that 

are taken before and after creating BRR 

weights to check that poststratification 

was successful. Appendix III provides 

syntax to create poststratified BRR 

weights expressed as integers. The 

syntax to create BRR weights in SAS is 

straightforward. 

SAS macros for the NCI Method 

As stated earlier, this report focuses 

on the estimation of single dietary 

components consumed daily (i.e., 

calcium, total fat, and total energy); a 

single dietary component consumed 

episodically (i.e., fruit); and a ratio of two 

dietary components consumed daily (i.e., 

percentage of energy from fat, also 

referred to as a bivariate model). 

Appendix IV summarizes the macros 

used for each nutrient and food being 

estimated. All macros use BRR variance 

estimation to calculate standard errors 

and confidence intervals. Below is a brief 

description of each macro. For more 

detailed information, see the User’s 

Guide (version 2.1) available from NCI 

(32). Appendix V provides sample code 

for the usual intake estimation of calcium 

as an example of univariate estimation, 

and Appendix VI provides sample code 

for the usual intake estimation of the 

percentage of energy from fat as an 

example of bivariate estimation. 

Estimating mean usual intake 

Given that NHANES is designed 

so that a single 24-hour recall can be 

used to estimate the mean consumption 

of a population or subgroup (4–8), 

the need to use more sophisticated 

modeling methods to estimate mean 

usual intake might seem unnecessary. 

However, the NCI macros build on each 

other, and it is necessary to estimate 

mean usual intake first, so that it is 

possible to next estimate a nutrient usual 

intake distribution. For a single dietary 

component, the MIXTRAN Macro fits 

nonlinear mixed effects models, using 

the SAS NLMIXED procedure to obtain 

parameter estimates and allow for the 

evaluation of covariate effects. The 

MIXTRAN Macro has the flexibility 

of running three different models based 

on the user-defined consumption pattern 

of the food or nutrient being modeled: 

(a) ubiquitously consumed nutrients, (b) 

episodically consumed nutrients where 

the probability of consumption and the 

amount consumed are not correlated, 

and (c) episodically consumed nutrients 

where the probability of consumption and 

the amount consumed are correlated. 

●		 For a single dietary component 

consumed daily (i.e., ubiquitously 

consumed), MIXTRAN runs 

a one-part or an amount-only 

model (AMOUNT). A commonly 

applied rule of thumb to determine 

whether a given dietary component 

is consumed ubiquitously or 

episodically can be determined 

as follows: If more than 5% of the 

analytic population reports zero 

intake of a given dietary component 

(i.e., nonconsumption on a given 

day), then that dietary component 

would be considered episodically 

consumed (53). In cases where less 

than 5% of the analytic population 

report zero intake, analysts may 

proceed with the amount-only 

models. However, it is standard to 

replace zero intake values with 

one-half the minimum amount 

consumed before running the 

amount-only model. This calculation 

is performed within the MIXTRAN 

Macro. Additionally, other cutoffs 

for zero intake, such as 10%, have 

been used to distinguish between 

ubiquitous or episodic consumption 

of a given dietary component. 

For dietary components that fall 

within the range of 5%–10% of the 

population reporting zero intake, 

analysts may wish to run both the 

one-part model or amount-only 

model and the two-part model and 

see if the results are materially 

different. Unfortunately, because of 

the complex survey design, it is not 

appropriate to try to select the bestfitting model based on typical output 

parameters, such as the Akaike 

information criterion (AIC) or –2 

log-likelihood, where lower values 

are indicative of better fit (53). Prior 

analyses have suggested that in the 

majority of these cases, the two-part 

model is preferred (52). 

●		 For a single dietary component that 

is consumed episodically, 
Series 2, No. 178  Page 7 

MIXTRAN runs either a correlated 

(CORR) or noncorrelated 

(NOCORR) two-part model. 

MIXTRAN first implements a 

mixed-effects logistic model to 

estimate the propensity to consume 

(e.g., the outcome is binary, modeling 

consumption or nonconsumption of a 

given dietary component on sampled 

days as realizations of Bernoulli 

random variables with success 

probabilities that differ across 

individuals). The second part of the 

model is a mixed-effects linear 

model to estimate the amount of the 

dietary component consumed. Users 

can specify whether the probability 

to consume is correlated with the 

amount consumed (CORR model) or 

not (NOCORR model). The 

correlation is between the personspecific random effects in both parts 

of the mixed-effects models: the 

probability part and the amount part. 

The probability to consume and the 

amount consumed may also be 

linked by the inclusion of a shared 

set of covariates in each model (53). 

Conceptually, the model equates 

usual intake to the product of an 

individual’s propensity to consume 

and the usual amount consumed by 

the individual on consumption days. 

In other words, people who eat a 

certain type of food tend to eat more 

of it because they like it or believe 

consumption is good for them. For 

example, people who eat whole 

grains are likely to eat larger 

amounts when they eat them. The 

same is true for less frequently 

consumed foods and small 

consumption amounts. If the model 

estimates a direct relationship 

between consumption amount and 

probability, then the estimated usual 

intake distribution will have more 

extreme higher values (i.e., a longer 

right tail) than if the model estimates 

no relationship, because in the 

former case, high probabilities will 

be more likely to be multiplied by 

large amounts and small probabilities 

by small amounts. 

In deciding whether to run a 

correlated or uncorrelated two-part model 

for episodically consumed nutrients 

or foods, analysts might consider 

several factors. First, it may be clear 

due to existing literature that frequent 

consumers of a given dietary component 

tend to consume greater amounts of that 

nutrient or food (5). Second, in cases 

where it may not be known whether the 

probability to consume and the amount 

consumed are correlated, analysts may 

wish to have a consistent method or 

standard to apply across all groups and 

dietary components. One such example 

would be if there are many nutrients 

and foods being estimated across 

multiple demographic groups or strata 

(52) where the correlation may only 

be evident in some subgroups or for 

some dietary components. In this case, 

it would be more appropriate to allow 

for the potential correlation between 

the probability to consume and the 

amount consumed parts of the models 

across all of the components and groups, 

than to assume they are not correlated. 

However, the correlated models are 

more computationally intensive, and 

will thus require more time to execute 

than the uncorrelated models. For details 

regarding an empirical approach to 

choosing a correlated or uncorrelated 

two-part model for episodically 

consumed foods, see Appendix VII. 

Estimating usual intake distributions 

and DRIs 

After the implementation of the 

MIXTRAN Macro, the DISTRIB 

Macro uses parameter estimates from 

MIXTRAN and a Monte Carlo method 

to estimate the distribution of usual 

intake for a given food or nutrient. Cut 

points can be specified to estimate the 

proportion of the population consuming 

less than or greater than a prespecified 

value, such as EAR or UL. Additional 

calculations must be performed to obtain 

standard errors and confidence intervals 

for the mean and percentiles from the 

usual intake distribution obtained from 

DISTRIB. Appendix V includes the 

macro BRR_PVALUE_CI Macro, 

which demonstrates how to use the 

estimates obtained from each BRR 

replication to produce estimated standard 

errors and confidence intervals. 

The DISTRIB Macro requires 

various parameters, unrelated to the 

variables that are generated and used in 

the modeling procedures, defined by the 

user, and can therefore vary from user to 

user. As a result, differences can occur in 

the estimation of usual intake of the same 

data. For example, differences in the 

number of repetitions or the seed number 

for the random number generator used 

in the Monte Carlo simulation can result 

in different estimates for the same data 

set. Analysts wishing to replicate their 

results should set a seed, or implement a 

reproducible process for varying seeds, 

for all of their analyses for consistency. 

See the NCI Method User’s Guide 

(version 2.1) for additional details about 

the DISTRIB Macro (32). 

Ratios 

When calculating the percentage of 

energy from fat, there are two ways to 

calculate the ratio. Estimates may not 

differ considerably from one another, but 

the choice of one over another depends 

on the question of interest. Although 

the ratio of usual intakes is typically 

preferred, as it more closely reflects 

the long-term intake of two dietary 

components, the estimation of the usual 

intake of ratios is simpler, as it is based 

on the univariate distribution of the ratio 

of daily intakes (54). In practice, both 

estimates tend to be very close. Freedman 

et al. (54) describes differences of less 

than one percentage point between the 

ratio of usual intakes and the usual 

intake of ratio estimates, examining the 

percentage of energy from saturated fat, 

and the percentage of energy from total 

fat. Freedman et al. (54) also provides 

guidance for quantifying the expected 

difference between the two methods 

based on the within-person correlations 

of each of the two dietary components 

and the within-person correlation 

between the two dietary components. 

1.	 The mean ratio is determined 

by calculating the ratio for each 

individual (e.g., fat kilocalories/ 

total kilocalories) and then taking 

the mean of the ratios using standard 

survey data analysis software, such 

as SAS proc SURVEYMEANS or 

SUDAAN proc DESCRIPT. 
Page 8  Series 2, No. 178 

2.	 The population ratio or ratio of 

means is where the mean fat and 

mean energy are calculated for the 

population, and then mean fat is 

divided by mean energy (55). 

For a ratio of usual intakes of two 

dietary components consumed daily 

(e.g., percentage of energy from fat), 

a set of SAS macros are available to 

examine the bivariate distribution. First, 

the NLMIXED UNIVARIATE Macro 

is used to fit a univariate measurement 

error model. This macro is implemented 

twice, once for the numerator (e.g., 

kilocalories from total fat) and once for 

the denominator (e.g., total kilocalories) 

of the ratio. Initial parameter estimates 

obtained from each of these two runs 

of the NLMIXED UNIVARIATE 

Macro are then used in the NLMIXED 

BIVARIATE Macro, which fits a 

bivariate measurement error model 

for both of the dietary components 

simultaneously. 

Similar to the process for 

estimating the distribution of a single 

dietary component described above, 

the parameter estimates from the 

NLMIXED BIVARIATE Macro 

are then used to create a Monte Carlo 

distribution of the usual intakes of two 

dietary components using the DISTRIB 

BIVARIATE Macro. This distribution 

can then be used to estimate the mean 

and percentiles of the population 

distribution of the ratio of usual intake 

using the PERCENTILES_SURVEY 

Macro. The PERCENTILES_SURVEY 

Macro allows users to specify a cut 

point (e.g., a DRI value expressed as a 

proportion) and will return the probability 

that the ratio of usual intakes is less 

than or equal to the specified DRI. For 

example, using this macro, analysts can 

estimate the proportion of the population 

that consumes 30% or less than their 

total kilocalories in the form of fat by 

specifying the cut point as 0.30 in the 

implementation of the PERCENTILES_ 

SURVEY Macro. SAS code for 

implementing the above macros are 

available in Appendix V. 

At this time, methods to examine 

the ratio of two episodically consumed 

dietary components, or between a 

ubiquitously consumed and episodically 

consumed dietary component, have not 

yet been published. If the usual intake 

ratio is the estimate of interest rather 

than the ratio of usual intakes, it can be 

calculated at the individual-day level 

and then modeled using MIXTRAN and 

DISTRIB, as described above. 

An additional consideration to weigh 

in choosing which value to report is that 

the usual intake of a ratio (equivalent to 

mean ratio) is more straightforward to 

estimate by using the macros MIXTRAN 

and DISTRIB, which are appropriate 

for estimating univariate distributions. 

In contrast, the ratio of usual intakes 

requires estimating the univariate 

measurement error model (NLMIXED 

UNIVARIATE) for each of the 

nutrients (e.g., fat kilocalories and total 

kilocalories) and subsequently estimating 

the bivariate measurement error model 

(NLMIXED BIVARIATE) for the two 

dietary components simultaneously (54). 

Additional consideration when 

using SAS macros: 

Transformations 

Because food or nutrient intake 

data are typically skewed, an additional 

macro, BOXCOX_SURVEY Macro, 

transforms a variable to approximate 

normality by applying a Box–Cox 

transformation. The MIXTRAN Macro 

described above applies a Box–Cox 

transformation within the macro itself, so 

this extra step is not entirely necessary. 

However, analysts may find it helpful to 

examine the transformation parameters 

from the BOXCOX_SURVEY Macro 

to determine if results may be sensitive 

to the particular transformation applied. 

Specifically, prior simulation studies 

have shown that various methods for 

estimating usual intake, including early 

versions of the NCI Method, may not 

perform well (i.e., may have larger mean 

bias) under certain scenarios where the 

lambda value obtained from the Box–Cox 

transformation is less than approximately 

0.15 (8). 

Convergence 

Depending upon the complexity of 

the models, the distribution of the data 

and the size of the subgroups, along with 

other factors, there are instances where 

one or more BRR replicates will fail to 

converge. There are several steps that 

analysts can take in these instances. 

Assuming the base run (replicate zero) is 

successful, and only a few replicate runs 

failed to converge, those replicates can be 

excluded from the BRR_PVALUE_CI 

Macro, and the degrees of freedom can 

be adjusted downward by subtracting the 

number of failed replicates. Another 

option is to adjust the starting values used 

in the MIXTRAN, DISTRIB, or the 

NLMIXED Macros or to change some 

of the default NLMIXED options, such 

as the maximum number of iterations or 

quadrature points used in the estimation 

procedures. See Appendix V for 

examples. Analysts should also consult 

the appropriate SAS manuals, 

documentation, and other SAS-support 

resources relating to mixed models (56, 

57). 

Application of NCI Method to 

estimate usual intake 

For usual intake estimation, version 

2.1 of the MIXTRAN and DISTRIB 

Macros were used, with poststratified 

BRR weights based on the Day 1 dietary 

weight, to account for day of the week, 

nonresponse, noncoverage, and unequal 

probabilities of selection, as well as 

survey design variables. 

For calcium, energy, fat, and 

the usual intake of ratios for the 

percentage of energy from fat, one-part 

AMOUNT-only models were used, 

as less than 1% of the population had 

no reported consumption over the 2 

days of recall. Because of the episodic 

nature of fruit consumption (roughly 

25% of respondents did not report 

fruit consumption on both days of 

recall), a two-part (PROBABILITY 

and AMOUNT) correlated (CORR) 

model was chosen, which allows for the 

probability to consume an item to be 

correlated with the amount consumed. 

However, convergence failures with the 

correlated model necessitated the use 

of the simpler uncorrelated two-part 

model (NOCORR). Usual intake with 

stratified models (by age and sex) were 

estimated to allow the macros to find 

a best fit transformation of the data. 
Therefore, no covariates were included 

in these analyses. For replicability of 

results, the following parameters were 

defined for the DISTRIB Macro: seed = 

5454768 and nsim_mc = 1000. However, 

for general analytic purposes, where 

exact replication is not strictly necessary, 

the seed for each DISTRIB run should 

change. For a detailed discussion about 

the parameters included in MIXTRAN 

and DISTRIB, see the User’s Guide (32). 

Statistical analyses were conducted 

using SAS version 9.3 (SAS Institute 

Inc.) for usual intake analysis and 

SUDAAN version 11 for all other 

analyses. For the current analysis, 

population means, percentiles, and 

standard errors calculated three ways are 

reported, using: (a) given day, (b) WPM, 

and (c) the NCI Method. Estimates are 

presented by age and sex. For given day 

and WPM calculations, Day 1 dietary 

weights were used to account for day of 

the week, nonresponse, noncoverage, 

and unequal probabilities of selection. 

Standard errors for given day and WPM 

estimates were estimated with Taylor 

series linearization to account for the 

complex, stratified sampling design. 

Results 

Table 2 presents given day, WPM, 

and the NCI Method estimates for 

calcium. The estimated means are within 

1 standard error of each other for all 

three methods, except for males aged 

12–19. Percentiles at the extremes of 

estimated intake vary and there is a 

tendency for estimated intake based on 

given day or WPM to be lower than the 

NCI Method at percentiles below the 

50th percentile (median) and greater than 

the NCI Method for percentiles above 

the median. There is also a tendency 

for the difference between estimated 

intakes based on the NCI Method and 

given day to be larger than the difference 

between the NCI Method and WPM. For 

illustrative purposes, Figure 1 shows this 

for men aged 40–59. A similar pattern 

can be seen when comparing estimates 

of the mean and percentiles for total fat 

(Table 3) and energy (Table 4). 

There was also variation in 

assessment of DRIs according to method. 

For example, the percentage of women 

aged 20–39 identified as consuming 

below EAR for calcium by given day, 

WPM, and the NCI Method were 51%, 

48%, and 42%, respectively (Figure 2 

and Table 2). Similar patterns can be 

observed among the percentage of the 

population above UL, where given day 

and WPM overestimate the percentage 

above UL compared with the NCI 

Method. For example, among men aged 

40–59, 5%, 4%, and 1% were identified 

as consuming above UL for calcium 

using given day, WPM, and the NCI 

Method, respectively (Table 2). The 

patterns in relation to these DRIs were 

not as consistent by age and sex as 

patterns of the distribution of intake. 

Table 5 presents estimates of the 

proportion of energy from total fat by 

five methods, because as noted in the 

Methods section, there are two ways 

to calculate ratios—mean ratio or a 

ratio of means—and three methods of 

estimation—given day, WPM, and the 

NCI Method. As for the other nutrients, 

the means are very similar according 

to method. However, unlike the single 

nutrients, there was somewhat more 

variability in estimated intake among the 

Series 2, No. 178  Page 9 

ratio methods, though only at percentiles 

below the median. For percentiles above 

the median, estimated intake was more 

similar according to method. As shown 

with the single nutrients, the NCI Method 

reduces the spread of the distribution, 

thus the proportion below the bottom 

threshold or above the top threshold 

is reduced. This is apparent when 

examining the ratio of the percentage of 

energy from fat within AMDR, where 

intake according to nonusual intake 

methods is generally underestimated. 

For example, among boys aged 6–11 

years, the estimates of the proportion 

within AMDR are 49%, 58%, and 52% 

for given day, ratio of mean WPM, and 

WPM mean ratio, respectively, and 70% 

and 78% for ratio of mean NCI Method 

and NCI Method mean ratio, respectively 

(Table 5). 

Table 6 presents estimates of fruit 

consumption for NHANES respondents 

aged 2 years and over from 2009 through 

2012. As with the nutrients examined 

above, the means for all three methods 

are similar. However, SUDAAN, similar 

to SAS survey procedures, is unable to 

estimate any quantile that is less than or 

equal to the percentage of data accounted 

for by the 0 values, thus SUDAAN 

was unable to calculate the 5th, 10th, 

Figure 1. Estimated distributions of calcium using three different methods for men aged 

40–59: National Health and Nutrition Examination Survey, 2011–2014 

NOTES: Usual intake distributions are estimated using the National Cancer Institute (NCI) Method. Given day estimates are 

from Day 1 dietary recall. This analysis includes men aged 40–59. Access Table I–1 at:

https://www.cdc.gov/nchs/data/series/sr_02/sr02_178_table.pdf#1. 

SOURCE: NCHS, National Health and Nutrition Examination Survey, 2011–2014.

 

 

 

 

 

 

 

 

 

 

 

 

 
NOTES: Given day estimates are from Day 1 dietary recall, within-person mean (WPM) estimates are from Days 1 and 2 dietary recalls, and usual intake distributions are estimated using 

the National Cancer Institute (NCI) Method. The estimated percentages of persons not meeting the estimated average requirement (EAR) for calcium with given day and WPM are shown 

for comparative purposes only. The recommended method to evaluate dietary adequacy is to compare estimates from usual intake methods, such as the NCI Method, with EAR. This 

analysis includes persons aged 2 years and over but excludes children who were reported to consume breast milk and women who were pregnant or lactating. Access Table I-2 at: https://

www.cdc.gov/nchs/data/series/sr_02/sr02_178_table.pdf#2. 

SOURCE: NCHS, National Health and Nutrition Examination Survey, 2011-2014. 

Page 10  Series 2, No. 178 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Figure 2. Percentage of U.S. population with calcium intake below estimated average requirement using different estimation methods, by 

age and sex: National Health and Nutrition Examination Survey, 2011–2014 
and sometimes the 25th percentile of 

intake for fruit consumption for some 

age groups. Where comparison between 

methods was possible, usual intake 

estimates for percentiles approximately 

below the 50th percentile were greater 

than given day and WPM estimates, 

while usual intake estimates for 

percentiles approximately above the 50th 

percentile tended to be smaller than given 

day or WPM estimates (Figure 3). This 

indicates compression of the tails of the 

distribution toward the mean, as seen 

with calcium, fat, energy, and percentage 

of energy from fat. For example, among 

boys aged 6–11 years, the estimated usual 

intake of fruit at the 25th percentile was 

0.1, 0.4, and 0.8 cup equivalents using 

given day, WPM, and the NCI Method, 

respectively. Among girls aged 2–5 years, 

estimates at the 90th percentile were 2.9, 

3.1, and 2.4 fruit cup equivalents using 

given day, WPM, and the NCI Method, 

respectively (Table 6). 

In addition to differences observed 

in the percentile estimates, the estimated 

percentage below the recommended 

minimum of consumption and above the 

recommended maximum consumption 

varied according to usual intake or 

nonusual intake methods. Except for 

the youngest age group (2–5 years), 

the percentage consuming less than the 

recommended intake was underestimated 

using nonusual intake methods compared 

with the NCI Method (Figure 4). In 

general, the NCI Method compresses the 

distribution toward the mean. Therefore, 

by comparison, nonusual intake methods 

will tend to overestimate the percentage 

of the population below the lower 

threshold and above the upper threshold. 

A different pattern was observed for 

usual fruit consumption. For example, 

for females aged 12–19 years, given-day 

methods estimated 45% consumed less 

than the recommended intake, WPM 

estimated 64%, and the NCI Method 

estimated 91%. Nonusual intake methods 

underestimated the percentage below the 

recommended minimum consumption 

level. As previously observed with other 

nutrients, the percentage of persons 

consuming above the recommended 

range tended to be overestimated when 

using nonusual intake methods (e.g., 

among women aged 60 and over, 18%, 

17%, and 11% were found to consume 

fruit above the recommended level, using 

given day, WPM, and the NCI Method, 

respectively) (Table 6). 

Discussion 

This report supplements existing 

information about the use of the NCI 

Method by providing guidance to analysts 

seeking to estimate the usual intake of 

dietary components using NHANES 

data. There are various analytic and 

programming considerations that an 

analyst may encounter when applying 

the NCI Method to NHANES dietary 

data, such as how to create and use BRR 

weights to account for the complex 

survey design. This report provides a 

description of these considerations as 

well as syntax and instructions to help 

analysts. 

When implementing the NCI 

Method with NHANES dietary data, 

there are many analytic decisions and 

considerations to make that are specific 

to the question of interest and population 

under examination. For example, 

different populations have different 

consumption patterns, so running an 

Series 2, No. 178  Page 11 

amount model (AMOUNT) in one 

population (e.g., adults) may require an 

episodic model (CORR or NOCORR) in 

another (e.g., children). The probability 

to consume and amount consumed might 

be correlated for some foods in some 

populations, but they may be uncorrelated 

in others. Variation in dietary intake 

patterns within a population could 

also necessitate different models and 

transformations by age or sex. For 

example, the lambda value of the Box– 

Cox transformation for a single nutrient 

may vary by age and sex. Rather than 

selecting a single fixed transformation 

parameter, it is better to conduct a 

stratified analysis, allowing for different 

transformation parameters by strata. This 

will result in better approximations of 

normality for each subgroup or stratum 

of interest, which is important because 

highly skewed distributions may result in 

convergence problems and increase bias 

(8). 

The shape of the distribution is 

critical for determining the proportion 

of a population at risk for deficiency 

or excess. Using the NHANES dietary 

data more simplistically (e.g., by using 

distributions from given day and WPM 

methods) to estimate usual intake can 

lead to erroneous conclusions about the 

magnitude of a public health problem, 

Figure 3. Estimated distributions of fruit intake using three different methods for girls aged 

2–5 years: National Health and Nutrition Examination Survey, 2009–2012 

NOTES: Given day estimates are from Day 1 dietary recall, within-person mean estimates are from Days 1 and 2 dietary 

recalls, and usual intake distributions are estimated using the National Cancer Institute (NCI) Method. Both the estimate 

and confidence interval for given day 5th percentile were set to zero because SUDAAN was unable to estimate it due to the 

large number of nonconsumers. This analysis includes persons aged 2–5 years but excludes children who were reported to 

consume breast milk. Access Table I–3 at: https://www.cdc.gov/nchs/data/series/sr_02/sr02_178_table.pdf#3. 

SOURCE: NCHS, National Health and Nutrition Examination Survey, 2009–2012.

 

 

 

 

 

 

 

 

 

 

 

 
Page 12  Series 2, No. 178 

Figure 4. Percentage of U.S. population with fruit intake below the lower recommended range, by age and sex: National Health and Nutrition 

Examination Survey, 2009–2012 

NOTES: Given day estimates are from Day 1 dietary recall, within-person mean (WPM) estimates are from Days 1 and 2 dietary recalls, and usual intake distributions are estimated using the 

National Cancer Institute (NCI) Method. The estimated percentages of persons consuming fruit below the lower recommended range with given day and WPM are shown for comparative 

purposes only. The recommended method to evaluate dietary adequacy is to compare estimates from usual intake methods, such as the NCI Method, with reference intakes. This analysis 

includes persons aged 2 years and over but excludes children who were reported to consume breast milk and women who were pregnant or lactating. Access Table I–4 at:

https://www.cdc.gov/nchs/data/series/sr_02/sr02_178_table.pdf#4. 

SOURCE: NCHS, National Health and Nutrition Examination Survey, 2009–2012.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Series 2, No. 178  Page 13 

such as inadequate or excessive intake 

of certain key nutrients. Using usual 

intake methods, such as the NCI Method 

with NHANES data, can lead to more 

appropriate conclusions about population 

dietary intake patterns, which could 

facilitate program and intervention 

planning by providing more accurate 

measurement of population dietary intake 

relative to DRIs. 

Similar to previous analyses 

comparing various methods for 

estimating dietary intake (5,8,9,28,31), 

this report demonstrates that overall, 

the means were approximately the 

same, irrespective of the method used to 

estimate intake. This is not surprising, 

since the population mean estimated 

using the NCI Method was designed 

to be consistent with estimates using 

a single 24-hour recall per individual. 

Similar to other reports (5,9,31,54,58), 

this report also shows that the NCI 

Method compresses the distribution 

toward the mean. For ubiquitously 

consumed items, such as calcium, energy, 

fat, and the percentage of energy from 

fat, the compression of the distribution 

toward the mean was most apparent 

when examining the proportion of the 

population below EAR or above UL. 

In this situation, methods that did not 

account for intra-individual variability, 

namely given day or WPM, tended to 

overestimate the percentage with the 

most extreme values of the distribution 

for ubiquitously consumed nutrients 

(5,8,9,23,28). Analysts seeking to 

evaluate the adequacy of diet (i.e., 

food and nutrient intake) in relation to 

reference standards using NHANES 

dietary data should use usual intake 

methods. The estimates based on 

single day and WPM in relation to DRI 

show how the magnitude of dietary 

insufficiency or excess could be 

overestimated if usual intake methods are 

not used (16–19). 

This report also examined the impact 

of estimating an episodically consumed 

food (e.g., fruit in cup equivalents) 

using 2009–2012 NHANES FPED files. 

Similar to the findings for ubiquitously 

consumed nutrients, estimates of means 

were similar with all three methods of 

estimation. Additionally, this report 

showed that the NCI Method compressed 

the distribution of consumption toward 

the mean for fruit, an episodically 

consumed food (4,8,9,12) but only for 

the right side of the distribution. The 

left side of the distribution is inflated 

by the large number of nonconsumers, 

and this changes the distribution such 

that the percentage below the lower 

threshold is underestimated using 

nonusual intake methods. This report 

only evaluated one example of an 

episodically consumed food in the U.S. 

population. Different patterns between 

results for the usual intake, given day, 

and WPM methods could occur for 

episodically consumed nutrients in 

different populations that have a different 

distribution than the example used 

here. The inflation of the left side of the 

distribution may also be due to the fact 

that practical considerations prevented 

the implementation of the correlated NCI 

model. If there truly was a significant 

positive correlation between probability 

and amount, the single-day and WPM 

distributions would reflect the correlation 

somewhat, while the uncorrelated NCI 

Method would not estimate enough 

people with more extreme higher values 

of intake (i.e., a long enough right tail of 

the distribution). The proportion of the 

population who reported no consumption 

of fruit over the 2 days of recall may also 

reflect a large number of nonconsumers 

of fruit. A similar left inflation in the 

distribution of estimated intake for a food 

with a large number of nonconsumers 

has been reported elsewhere (8). The 

expectation that given day and WPM 

methods tend to overestimate the 

extreme values of the distribution does 

not necessarily apply for episodically 

consumed foods where there is a large 

percentage of nonconsumers. 

NHANES uses specific age, sex, 

and race and Hispanic-origin groupings 

as part of the standard sampling frame. 

The age categories in the sampling 

frame differ from those used by the 

National Academies of Science, 

Engineering and Medicine (formerly 

the Institute of Medicine) to establish 

DRIs (17). The difference between the 

two age categorizations would likely 

have minimal impact on estimates of 

usual intake using the NCI Method. 

However, analysts should be aware of 

the difference. While it is often standard 

to report results specific to the NHANES 

sampling frame-based age groups (e.g., 

2–5, 6–11, and 12–19 years), the use of 

the DRI age and sex subgroups is more 

straightforward if the goal of the analysis 

is to calculate population proportions at 

risk of deficiency or excess. 

Limitations 

These analyses were limited to 

nutrients (calcium, energy, fat, and the 

percentage of energy from fat) that 

were consumed by nearly all NHANES 

participants every day and a single 

food item, fruit, for which 25% of the 

population reported no consumption. 

These two types of analyses could be 

described as the most straightforward 

situations for estimating usual intake, 

because their distributions were 

approximately normal or could be 

transformed to normality. Foods and 

nutrients that are infrequently consumed, 

such as nuts or specific fruits and 

vegetables, can present problems during 

analysis, because the distribution is 

skewed with a relatively larger proportion 

of nonconsumers or heavy consumers. 

If a Box–Cox transformation is not 

sufficient to transform the distribution 

of observed intakes to approximate 

normality (e.g., for multimodal or 

extremely heavy-tailed distributions), 

a key model assumption is violated, 

and the NCI Method may not be able to 

overcome this limitation. 

Computing power and computational 

intensity is another consideration when 

using NHANES data to estimate usual 

intake with the NCI Method. As the 

complexity of the research question 

increases, so does the time it takes to 

run a model. Running these models on 

a single computer requires considerable 

processing power, and even with small 

age and sex groups, the time to return 

a distribution for a simple AMOUNT 

model was often measured in minutes 

(10–15 on average on a 32GB RAM 

machine) for two survey cycles of 

NHANES data, roughly 16,000 

respondents. As models became more 
Page 14  Series 2, No. 178 

 

 

 

 

 

 

 

 

Figure 5. The relationship between bias and precision when comparing given day with usual 

intake methods for estimating intake 

complex, for example, correlated fruit 

models for single age and sex groups, 

it was necessary to run models on a 

computer platform with increased 

processing power and speed. Despite 

this, it often took hours to run a single 

age and sex model. Analysts could 

explore other ways to reduce processing 

time depending on available statistical 

computing resources. 

Another aspect that deserves mention 

is the need to scrutinize SAS logs to 

troubleshoot when the macros do not 

work as expected. It is not uncommon 

for some of the weight replicates to fail 

to converge. If this happens for a small 

number of replicates (e.g., roughly 3–4 

replicates out of 32 for an NHANES data 

set that includes 4 years), it is reasonable 

to delete these replicates and estimate 

standard errors with the remaining 

replicates (reducing the degrees of 

freedom when calculating standard 

errors). If more than a small number fail 

to converge, it is necessary to look more 

closely at why convergence is a problem 

and explore alternative solutions. 

Importantly, the NCI Method does 

not address systematic error or bias in 

self-reported dietary intake. Estimates of 

certain dietary components, energy intake 

most prominently, have long been shown 

to be misreported in 24-hour recalls. The 

degree of misreporting varies by dietary 

component and population characteristics 

(12). Thus, it is well established that the 

unbiasedness assumption underlying 

the NCI Method (and other methods 

of estimating dietary intake) is likely 

violated in many circumstances. While 

usual intake estimates are more precise 

than estimates based on a single day of 

recall, residual systematic error may 

not lead to a more accurate or less 

biased measure (59) (Figure 5). Ideally, 

methods to estimate usual or habitual 

dietary intake would produce results 

characterized by panel B in Figure 5. 

Realistically, panel D shows the results 

of applying these methods: an increase 

in precision, but bias is not addressed. 

Results of any analyses of dietary intake 

should be interpreted cautiously and 

appropriately in the context of known 

limitations related to the data and 

methods (12). 

Finally, this report demonstrates two 

of the most common and straightforward 

examples of using NHANES data to 

estimate usual intake of a given dietary 

component with the NCI Method— 

describing food and nutrient distributions 

in a population and identifying the 

proportion of a population consuming 

above or below a threshold. There 

are other applications of the NCI 

methodology that are beyond the scope 

of this report, such as modeling intake 

of more than two potentially correlated 

dietary components through multivariate 

measurement error models and outputting 

conditional mean intakes for use in 

modeling associations between dietary 

intake and health outcomes. Further 

discussion of these other applications and 

related methods is provided elsewhere 

(12,24,34,36,60). 

Summary 

This report describes how to adapt 

the NCI Method to estimate usual intake 

distributions for foods and nutrients using 

NHANES dietary data. Detailed SAS 

coding and corresponding output tables 

are provided to data users to replicate 

these analyses and implement the NCI 

Method with NHANES data. 

This report shows that the means 

for calcium, fat, energy, percentage 

of energy from fat, and fruit using 

NHANES dietary data were similar 

across all three approaches. For this 

reason, population estimates of mean 

intake using NHANES can reasonably 

be estimated using given-day methods. 

In this situation, usual intake methods 

offer no advantages over the simpler 

method, while increasing complexity. In 

contrast, the distributions estimated by 

given day and WPM methods were wider 

than those estimated by the NCI Method. 

When using NHANES data to estimate 

the proportion of the population above 

or below a certain limit for adequate 

or excess nutrient or food intake, if 

the assumptions of the NCI Method 

are satisfied, the wider distributions of 

the WPM or given-day methods can 

overestimate or underestimate these 

proportions depending on whether 

the nutrient or food is ubiquitously or 

episodically consumed. This leads to 

biased estimates of the proportion of 

the population at risk of insufficiency or 
excess. For distributions and comparisons 

with DRIs, usual intake methods are 

necessary to reduce within-person 

variation and produce a more precise 

estimate when using NHANES dietary 

data. Beyond analytical applications, 

program and intervention planning that 

use NHANES data based on methods that 

do not use usual intake estimates may 

be based on imprecise estimates of the 

magnitude of various population dietary 

shortfalls or excesses. 

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for analysis of a single dietary 

component. Available from: 

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usualintakes/macros_single.html. 

36.	 National Cancer Institute. Usual 

dietary intakes: SAS macros for 

estimating ratios of two dietary 

components that are consumed 

nearly every day. Available from: 

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usualintakes/macros_double.html. 

37.	 CDC. National Health and Nutrition 

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38.	 Moshfegh AJ, Rhodes DG, Baer DJ, 

Murayi T, Clemens JC, Rumpler 

WV, et al. The US Department of 

Agriculture automated multiple-pass 

method reduces bias in the collection 

of energy intakes. Am J Clin Nutr 

88(2):324–32. 2008. 

39. Herrick KA, Rossen LM, Nielsen 

SJ, Branum AM, Ogden CL. Fruit 

consumption by youth in the United 

States. Pediatrics 136(4):664–71. 

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40. National Center for Health Statistics. 

NHANES response rates and 

population totals. Available from: 

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response_rates_cps.htm. 

41.	 Curtain LR, Mohadjer LK, 

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Nhanes/2011-2012/DR1TOT_G.htm. 

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data documentation, codebook, and 

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data documentation, codebook, and 

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Nhanes/2013-2014/DR2TOT_H.htm. 

45. United States Department of 

Agriculture. Food patterns 

equivalents database: Databases 

and SAS data sets. Available from: 

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food-surveys-research-group/docs/ 

fped-databases/. 

46.	 Institute of Medicine. Dietary 

reference intakes: Applications in 

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47.	 Institute of Medicine of the National 

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intakes for calcium and vitamin 

D. Washington, DC: National 

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48. United States Department of 

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49. Institute of Medicine. Dietary 

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54. Freedman LS, Guenther PM, Dodd 

KW, Krebs-Smith SM, Midthune 

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measurement-error/#session. 

60. Prentice RL, Tinker LF, Huang 

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Page 18  Series 2, No. 178 

Table 1. SAS macros developed by National Cancer Institute to run nonlinear mixed models used in this report 

Type of dietary component 

to be modeled Macro Function of macro 

Single dietary component consumed daily 

or episodically 

MIXTRAN Fits model to obtain parameter estimates and allows for evaluation of 

covariate effects. 

DISTRIB Uses parameter estimates from MIXTRAN and Monte Carlo method to 

estimate distribution of usual intake for a food or nutrient. 

Ratio of two dietary components where 

desired estimate is ratio of usual intakes 

NLMIXED UNIVARIATE Fits a univariate measurement error model for a single dietary 

component. Primary purpose is to provide initial parameter estimates 

for the macro NLMIXED BIVARIATE. 

NLMIXED BIVARIATE Fits a bivariate measurement error model for two dietary components 

simultaneously. 

DISTRIB BIVARIATE Uses parameter estimates from NLMIXED BIVARIATE Macro to 

estimate distribution of the ratio of usual intakes of two dietary 

components. 

PERCENTILES_SURVEY Uses distribution generated by DISTRIB BIVARIATE Macro to 

calculate mean and percentiles of population distribution of usual 

intake. 

Supplementary macros 

Single dietary component consumed daily 

or episodically 

BOXCOX_SURVEY Uses Box–Cox transformation to transform variable to approximate 

normality. 

Single dietary component or ratio of 

two dietary components (ratio of usual 

intakes) 

BRR_PVALUE_CI Performs balanced repeated replication variance estimation and 

calculates standard errors, confidence intervals, and p values for 

user-specified parameters of interest using estimates from DISTRIB 

macros (DISTRIB and DISTRIB BIVARIATE). 

SOURCE: Usual Dietary Intakes: SAS macros for the National Cancer Institute Method (see reference 34 in the report). 
Series 2, No. 178  Page 19 

Table 2. Calcium (grams per day): Mean, percentile, and percentage of persons meeting dietary reference intakes, calculated by given day, 

within-person mean, and the National Cancer Institute Method in the U.S. population aged 2 years and over: National Health and Nutrition 

Examination Survey, 2011–2014 

Sex, age group (years), Mean 5th 10th 25th 50th 

and method (standard error) (standard error) (standard error) (standard error) (standard error) 

Males 

2–5 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,005.5 (36.4) 257.0 (30.1) 394.8 (34.4) 647.4 (31.0) 899.2 (31.4) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 987.0 (33.0) 379.6 (24.0) 471.1 (26.4) 656.4 (23.5) 904.9 (33.4) 

National Cancer Institute Method . . . . . . . . . . . 982.6 (29.8) 518.2 (20.7) 600.0 (19.6) 754.4 (20.9) 952.1 (28.4) 

6–11 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,156.2 (25.5) 387.7 (30.4) 523.9 (29.5) 747.3 (25.3) 1,065.5 (31.3) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,170.8 (21.4) 483.0 (27.6) 596.4 (20.3) 849.5 (22.7) 1,122.0 (31.3) 

National Cancer Institute Method . . . . . . . . . . . 1,168.6 (21.0) 683.0 (43.0) 774.4 (36.9) 938.2 (25.8) 1,143.1 (19.9) 

12–19 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,248.5 (31.4) 331.1 (25.2) 447.7 (30.8) 688.0 (31.7) 1,088.5 (24.2) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,195.4 (25.9) 423.2 (25.8) 530.1 (21.1) 775.8 (22.1) 1,090.5 (32.7) 

National Cancer Institute Method . . . . . . . . . . . 1,195.7 (27.1) 601.0 (30.0) 703.2 (27.8) 896.4 (24.3) 1,149.1 (24.9) 

20–39 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,202.4 (29.9) 339.1 (23.1) 455.1 (15.3) 674.3 (19.6) 1,029.1 (25.6) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,185.8 (25.5) 412.7 (14.7) 516.3 (15.7) 721.9 (20.2) 1,065.5 (21.0) 

National Cancer Institute Method . . . . . . . . . . . 1,180.7 (22.6) 571.9 (22.9) 672.3 (20.6) 866.3 (16.8) 1,124.9 (19.2) 

40–59 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,080.1 (24.0) 303.7 (21.4) 384.9 (16.9) 622.1 (23.8) 964.3 (34.0) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,071.3 (21.5) 377.0 (16.3) 461.5 (18.7) 671.0 (20.5) 988.6 (19.3) 

National Cancer Institute Method . . . . . . . . . . . 1,075.6 (19.7) 524.8 (23.3) 615.9 (20.0) 791.5 (15.0) 1,026.3 (17.2) 

60 and over 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 974.9 (18.6) 263.9 (18.3) 360.1 (15.6) 581.0 (16.7) 858.5 (23.2) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 972.3 (15.8) 355.9 (14.8) 434.0 (16.8) 623.4 (12.7) 879.8 (18.1) 

National Cancer Institute Method . . . . . . . . . . . 974.4 (14.7) 457.0 (21.5) 540.3 (19.1) 704.0 (13.9) 924.9 (11.5) 

Females 

2–5 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 937.6 (25.6) 330.8 (26.1) 431.8 (21.2) 632.6 (24.5) 853.4 (30.0) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 935.4 (21.0) 383.4 (22.9) 470.2 (23.8) 634.1 (26.8) 895.0 (25.2) 

National Cancer Institute Method . . . . . . . . . . . 934.0 (24.7) 530.1 (25.3) 601.4 (24.1) 734.6 (22.6) 906.1 (23.7) 

6–11 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 982.3 (23.1) 304.1 (24.1) 440.7 (24.8) 624.3 (27.7) 907.6 (31.0) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,004.4 (17.7) 430.2 (22.5) 527.3 (21.1) 706.4 (22.9) 941.6 (28.3) 

National Cancer Institute Method . . . . . . . . . . . 1,004.9 (15.3) 661.7 (35.2) 727.2 (30.6) 843.9 (22.1) 988.6 (15.4) 

12–19 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 889.9 (22.7) 227.5 (20.3) 298.4 (17.3) 503.5 (27.4) 819.3 (34.5) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 866.6 (20.6) 273.5 (28.3) 358.0 (15.5) 569.1 (18.1) 834.4 (20.3) 

National Cancer Institute Method . . . . . . . . . . . 869.2 (19.8) 424.2 (25.3) 501.1 (23.4) 646.4 (19.9) 836.1 (19.0) 

20–39 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 892.1 (20.7) 257.5 (9.60) 333.7 (11.3) 543.3 (21.9) 788.3 (26.6) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 876.2 (16.1) 317.1 (14.5) 406.4 (13.7) 570.6 (13.3) 820.0 (16.4) 

National Cancer Institute Method . . . . . . . . . . . 877.1 (15.4) 499.3 (27.8) 565.4 (24.9) 689.9 (19.2) 850.3 (15.2) 

40–59 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 853.1 (13.1) 232.6 (17.5) 329.9 (13.6) 499.1 (14.8) 773.0 (18.9) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 852.0 (12.0) 309.0 (20.7) 397.5 (12.3) 560.4 (9.3)0 772.1 (18.3) 

National Cancer Institute Method . . . . . . . . . . . 850.6 (11.4) 430.0 (18.9) 500.0 (17.4) 634.5 (14.0) 813.1 (10.6) 

60 and over 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 810.2 (20.6) 245.1 (17.8) 321.8 (19.0) 496.2 (21.4) 724.9 (18.0) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 811.3 (15.5) 319.4 (16.5) 397.1 (14.5) 563.1 (15.6) 750.1 (16.9) 

National Cancer Institute Method . . . . . . . . . . . 810.9 (13.3) 433.1 (15.1) 498.0 (13.8) 621.7 (11.4) 781.7 (11.6) 

See footnotes at end of table. 
Page 20  Series 2, No. 178 

Table 2. Calcium (grams per day): Mean, percentile, and percentage of persons meeting dietary reference intakes, calculated by given day, 

within-person mean, and the National Cancer Institute Method in the U.S. population aged 2 years and over: National Health and Nutrition 

Examination Survey, 2011–2014—Con. 

Sex, age group (years), 75th 90th 95th Percent less than EAR Percent more than UL 

and method (standard error) (standard error) (standard error) (standard error) (standard error) 

Males 

2–5 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,271.9 (48.6) 1,743.7 (95.2) 1,992.1 (111.9) 27.4 (2.9) 1.7 (0.7) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,226.6 (34.7) 1,614.3 (62.7) 1,794.0 (95.7) 24.5 (2.8) 0.6 (0.3) 

National Cancer Institute Method . . . . . . . . . . . 1,177.7 (41.2) 1,402.9 (56.7) 1,551.8 (67.6) 16.5 (1.7) 0.0 (0.0) 

6–11 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,462.4 (42.2) 1,867.5 (47.6) 2,178.1 (99.1) 39.1 (1.9) 1.0 (0.4) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,420.6 (34.6) 1,785.9 (57.6) 2,005.7 (82.3) 35.3 (2.2) 1.3 (0.6) 

National Cancer Institute Method . . . . . . . . . . . 1,371.1 (36.0) 1,596.4 (61.9) 1,742.3 (80.9) 26.7 (2.4) 0.0 (0.1) 

12–19 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,632.6 (59.4) 2,275.8 (75.4) 2,677.2 (118.1) 49.0 (1.9) 4.6 (1.2) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,481.1 (39.2) 1,958.2 (83.7) 2,467.9 (128.7) 48.5 (2.2) 1.7 (0.9) 

National Cancer Institute Method . . . . . . . . . . . 1,444.5 (37.6) 1,749.4 (58.8) 1,949.3 (75.0) 40.8 (2.9) 0.1 (0.1) 

20–39 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,538.1 (33.5) 2,124.6 (70.7) 2,441.5 (125.8) 33.3 (1.5) 4.8 (0.9) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,462.3 (28.6) 1,952.7 (78.0) 2,342.4 (107.6) 30.5 (1.6) 3.6 (0.9) 

National Cancer Institute Method . . . . . . . . . . . 1,434.0 (34.8) 1,760.3 (58.4) 1,980.2 (76.7) 19.2 (1.5) 0.8 (0.4) 

40–59 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,400.9 (34.2) 1,966.8 (51.1) 2,203.1 (67.3) 38.6 (2.0) 5.2 (0.8) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,363.7 (37.3) 1,767.2 (57.3) 2,089.1 (78.4) 34.3 (2.0) 3.6 (0.8) 

National Cancer Institute Method . . . . . . . . . . . 1,305.9 (31.5) 1,598.1 (50.6) 1,795.8 (64.3) 25.9 (1.5) 1.2 (0.4) 

60 and over 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,254.7 (24.5) 1,698.7 (59.7) 2,053.3 (58.0) 51.9 (1.8) 6.0 (0.8) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,211.3 (22.3) 1,579.9 (40.2) 1,880.4 (90.2) 49.5 (1.9) 4.3 (0.7) 

National Cancer Institute Method . . . . . . . . . . . 1,190.4 (25.5) 1,472.4 (47.7) 1,662.9 (65.4) 44.4 (1.3) 1.3 (0.5) 

Females 

2–5 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,158.4 (35.8) 1,533.8 (90.3) 1,813.7 (81.1) 30.3 (2.6) 0.9 (0.4) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,145.3 (43.3) 1,480.0 (46.9) 1,648.8 (42.9) 25.3 (2.2) 0.2 (0.1) 

National Cancer Institute Method 1,102.8 (32.1) 1,301.7 (47.3) 1,434.4 (59.6) 17.8 (2.5) 0.0 (0.0) 

6–11 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,259.4 (42.5) 1,646.3 (58.4) 1,839.4 (78.9) 53.7 (2.4) 0.9 (0.6) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,262.6 (25.9) 1,564.4 (43.4) 1,820.1 (51.0) 49.7 (2.4) 0.0 (0.0) 

National Cancer Institute Method . . . . . . . . . . . 1,147.3 (22.0) 1,304.8 (37.8) 1,405.3 (49.4) 43.6 (2.6) 0.0 (0.0) 

12–19 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,216.1 (45.6) 1,519.7 (32.9) 1,724.1 (60.6) 67.5 (2.1) 0.2 (0.2) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,108.4 (31.9) 1,395.2 (42.7) 1,616.9 (59.6) 71.4 (2.3) 0.2 (0.2) 

National Cancer Institute Method . . . . . . . . . . . 1,055.3 (27.4) 1,280.0 (42.6) 1,427.3 (54.2) 74.4 (2.2) 0.0 (0.0) 

20–39 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,166.9 (33.1) 1,507.5 (25.7) 1,797.4 (84.7) 50.5 (1.9) 1.1 (0.4) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,104.1 (25.6) 1,408.4 (26.1) 1,713.7 (72.1) 47.8 (1.7) 0.2 (0.1) 

National Cancer Institute Method . . . . . . . . . . . 1,034.8 (21.6) 1,223.1 (35.9) 1,347.5 (46.9) 42.1 (2.5) 0.0 (0.0) 

40–59 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,097.6 (27.4) 1,478.2 (43.4) 1,757.7 (40.7) 59.3 (1.5) 1.7 (0.5) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,073.5 (17.4) 1,376.5 (39.9) 1,641.9 (31.9) 61.3 (1.6) 1.1 (0.3) 

National Cancer Institute Method . . . . . . . . . . . 1,025.8 (15.2) 1,249.7 (29.3) 1,400.1 (41.3) 59.5 (1.4) 0.1 (0.1) 

60 and over 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,044.6 (33.1) 1,372.3 (37.1) 1,643.2 (71.4) 72.9 (1.6) 1.8 (0.6) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 989.5 (23.6) 1,290.1 (29.2) 1,519.1 (37.1) 75.8 (1.8) 0.7 (0.2) 

National Cancer Institute Method . . . . . . . . . . . 968.5 (19.0) 1,160.9 (31.1) 1,288.6 (40.0) 78.2 (2.0) 0.1 (0.0) 

0.0 Quantity more than zero but less than 0.05. 

NOTES: EAR is estimated average requirement and UL is tolerable upper limit. Given day estimates are from Day 1 dietary recall, within-person mean estimates are from Days 1 and 2 dietary recalls, 


and usual intake distributions are estimated using the National Cancer Institute Method. 


The recommended way to evaluate the adequacy of dietary intake in relation to reference standards is by using Methods that estimate usual intake and therefore account for within-person variation.
 

Comparing estimates based on given day and within-person mean with reference standards is not recommended. They are presented to illustrate the inaccuracy associated with using methods that
 

do not account for within-person variation (see references 12–15 in the report). Children who were reported to consume breast milk and women who were pregnant or lactating were excluded from
 

the data analysis.
 

SOURCE: NCHS, National Health and Nutrition Examination Survey, 2011–2014. 

Table 3. Total fat (grams per day): Mean and percentile, calculated by given day, within-person mean, and the National Cancer Institute Method in the U

aged 2 years and over: National Health and Nutrition Examination Survey, 2011–2014 

Sex, age group (years), Mean 5th 10th 25th 50th 75th 

and method (standard error) (standard error) (standard error) (standard error) (standard error) (standard error) (sta

Males 

2–5 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57.5 (1.4) 27.9 (1.1) 31.1 (0.9) 40.4 (0.8) 52.6 (0.9) 70.5 (2.1) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . 56.2 (1.2) 26.7 (1.7) 32.8 (1.2) 40.3 (1.0) 52.2 (1.1) 67.0 (1.2) 

National Cancer Institute Method . . . . . . . . . . . 56.4 (1.2) 33.2 (1.7) 37.4 (1.4) 45.3 (1.1) 55.1 (1.0) 66.1 (1.9) 

6–11 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76.3 (1.6) 32.4 (1.6) 37.9 (1.8) 53.1 (1.2) 68.9 (1.6) 92.4 (3.0) 1

Within-person mean . . . . . . . . . . . . . . . . . . . . . 74.6 (1.4) 35.1 (2.1) 42.7 (1.4) 55.0 (1.5) 68.3 (1.6) 89.0 (2.3) 1

National Cancer Institute Method . . . . . . . . . . . 74.7 (1.4) 47.0 (2.4) 52.2 (2.0) 61.5 (1.5) 73.2 (1.3) 86.2 (2.1) 

12–19 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.3 (2.1) 31.7 (1.7) 40.5 (1.2) 56.6 (1.8) 80.8 (2.2) 115.0 (4.9) 1

Within-person mean . . . . . . . . . . . . . . . . . . . . . 87.4 (2.2) 35.7 (1.6) 42.5 (1.8) 59.9 (1.8) 80.1 (1.7) 107.9 (4.0) 1

National Cancer Institute Method . . . . . . . . . . . 87.5 (2.3) 48.1 (2.9) 55.0 (2.6) 67.8 (2.0) 84.5 (2.0) 104.0 (3.5) 1

20–39 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101.2 (1.3) 32.2 (1.5) 43.6 (1.3) 65.2 (1.1) 94.7 (1.6) 127.4 (2.2) 1

Within-person mean . . . . . . . . . . . . . . . . . . . . . 97.3 (1.3) 39.3 (1.4) 50.0 (1.6) 67.3 (1.0) 89.9 (1.4) 116.9 (2.0) 1

National Cancer Institute Method . . . . . . . . . . . 97.1 (1.2) 55.6 (2.0) 63.2 (1.7) 77.2 (1.3) 94.8 (1.1) 114.4 (2.0) 1

40–59 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97.4 (1.9) 30.9 (1.6) 41.0 (2.1) 60.5 (1.1) 88.3 (2.6) 123.3 (3.4) 1Within-person mean . . . . . . . . . . . . . . . . . . . . . 93.7 (1.5) 37.6 (1.2) 46.6 (2.1) 65.3 (1.3) 88.5 (2.0) 114.8 (2.7) 1

National Cancer Institute Method . . . . . . . . . . . 94.2 (1.4) 53.0 (1.9) 60.5 (1.8) 74.2 (1.6) 91.7 (1.4) 111.4 (1.7) 1

60 and over 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84.2 (1.3) 28.4 (1.3) 36.6 (1.0) 54.7 (1.3) 79.6 (1.8) 105.9 (2.7) 1

Within-person mean . . . . . . . . . . . . . . . . . . . . . 82.7 (1.2) 32.5 (1.5) 42.0 (1.7) 57.3 (1.3) 78.6 (1.2) 100.6 (1.6) 1

National Cancer Institute Method . . . . . . . . . . . 83.0 (1.2) 45.0 (1.4) 51.8 (1.3) 64.6 (1.0) 80.7 (1.1) 98.9 (1.6) 1

Females 

2–5 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.6 (1.2) 19.7 (1.1) 26.2 (1.2) 34.7 (1.4) 49.1 (1.3) 65.7 (1.8) Within-person mean . . . . . . . . . . . . . . . . . . . . . 52.7 (1.1) 25.5 (1.2) 28.9 (0.9) 37.9 (1.2) 49.5 (1.2) 64.9 (2.6) 

National Cancer Institute Method . . . . . . . . . . . 52.5 (1.3) 32.6 (1.4) 36.3 (1.2) 43.0 (0.9) 51.4 (1.2) 60.8 (2.0) 

6–11 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68.8 (1.2) 27.5 (1.5) 34.6 (1.6) 47.7 (1.5) 66.2 (1.5) 83.9 (2.1) 1

Within-person mean . . . . . . . . . . . . . . . . . . . . . 67.9 (1.1) 32.4 (2.0) 40.4 (1.2) 50.4 (1.4) 65.8 (1.2) 80.4 (1.6) 1

National Cancer Institute Method . . . . . . . . . . . 67.7 (1.0) 49.3 (3.3) 52.9 (2.8) 59.3 (1.9) 67.1 (1.0) 75.4 (1.7) 

12–19 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65.9 (1.8) 16.0 (2.8) 25.7 (2.4) 39.3 (1.9) 61.3 (1.6) 84.1 (2.1) 1

Within-person mean . . . . . . . . . . . . . . . . . . . . . 63.9 (1.2) 23.7 (3.0) 30.2 (1.6) 44.3 (1.7) 61.4 (1.8) 81.0 (2.0) 

National Cancer Institute Method . . . . . . . . . . . 64.1 (1.1) 38.1 (3.9) 43.0 (3.4) 52.0 (2.4) 62.9 (1.3) 75.0 (1.6) 

20–39 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74.1 (1.0) 23.2 (1.4) 31.7 (1.0) 46.5 (1.2) 66.5 (1.4) 95.4 (2.0) 1

Within-person mean . . . . . . . . . . . . . . . . . . . . . 70.3 (0.9) 26.5 (1.7) 33.3 (1.5) 47.2 (1.1) 65.3 (1.1) 87.3 (1.6) 1National Cancer Institute Method . . . . . . . . . . . 70.3 (0.9) 41.6 (2.3) 46.8 (2.1) 56.4 (1.5) 68.6 (0.9) 82.3 (1.2) 

40–59 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70.3 (0.8) 20.9 (1.3) 27.8 (1.5) 43.3 (1.3) 65.3 (0.8) 89.1 (1.4) 1

Within-person mean . . . . . . . . . . . . . . . . . . . . . 69.5 (0.9) 25.5 (1.5) 33.8 (1.6) 48.2 (0.9) 65.4 (0.9) 87.0 (1.7) 1National Cancer Institute Method . . . . . . . . . . . 69.6 (1.0) 36.8 (1.6) 42.7 (1.4) 53.6 (1.1) 67.5 (1.0) 83.3 (1.4) 

60 and over 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.9 (1.3) 18.7 (1.0) 26.8 (1.2) 40.1 (1.1) 57.7 (1.6) 80.8 (2.0) 1

Within-person mean . . . . . . . . . . . . . . . . . . . . . 63.4 (1.0) 24.7 (0.9) 30.5 (1.1) 42.8 (1.1) 60.2 (0.9) 79.3 (1.3) 1

National Cancer Institute Method . . . . . . . . . . . 63.6 (0.9) 33.5 (1.5) 38.7 (1.3) 48.6 (1.0) 61.4 (0.8) 76.1 (1.4) 

NOTES: Given day estimates are from Day 1 dietary recall, within-person mean estimates are from Days 1 and 2 dietary recalls, and usual intake distributions are estimated using the National Cancer Institute Methconsume breast milk and women who were pregnant or lactating were excluded from the data analysis. SOURCE: NCHS, National Health and Nutrition Examination Survey, 2011–2014. 
Table 4. Energy (kilocalories per day): Mean and percentile, calculated by given day, within-person mean, and the National Cancer Institute Method in 

years and over: National Health and Nutrition Examination Survey, 2011–2014 

Sex, age group (years), Mean 5th 10th 25th 50th 75th 

and method (standard error) (standard error) (standard error) (standard error) (standard error) (standard error) (sta

Males 

2–5 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,613 (25) 859 (24) 986 (31) 1,251 (30) 1,542 (35) 1,877 (30) 2

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,589 (21) 926 (40) 1,055 (25) 1,270 (28) 1,529 (25) 1,857 (22) 2

National Cancer Institute Method . . . . . . . . . . . 1,590 (19) 1,055 (37) 1,158 (33) 1,342 (25) 1,567 (19) 1,813 (25) 2

6–11 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,061 (31) 1,082 (51) 1,264 (32) 1,589 (37) 1,982 (37) 2,404 (38) 2

Within-person mean . . . . . . . . . . . . . . . . . . . . . 2,033 (26) 1,207 (30) 1,368 (35) 1,643 (24) 1,986 (37) 2,354 (43) 2

National Cancer Institute Method . . . . . . . . . . . 2,036 (25) 1,463 (49) 1,578 (42) 1,776 (30) 2,016 (25) 2,273 (40) 2

12–19 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,453 (36) 1,038 (46) 1,256 (46) 1,702 (49) 2,211 (37) 2,965 (54) 3

Within-person mean . . . . . . . . . . . . . . . . . . . . . 2,327 (44) 1,124 (52) 1,290 (58) 1,732 (44) 2,190 (29) 2,744 (76) 3

National Cancer Institute Method . . . . . . . . . . . 2,326 (48) 1,450 (78) 1,612 (66) 1,906 (47) 2,276 (43) 2,692 (78) 3

20–39 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,716 (30) 1,225 (36) 1,454 (44) 1,914 (34) 2,570 (33) 3,320 (61) 4

Within-person mean . . . . . . . . . . . . . . . . . . . . . 2,608 (30) 1,290 (33) 1,518 (33) 1,917 (29) 2,491 (29) 3,058 (43) 3

National Cancer Institute Method . . . . . . . . . . . 2,602 (29) 1,582 (41) 1,775 (36) 2,124 (27) 2,553 (27) 3,026 (46) 3

40–59 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,560 (45) 1,099 (57) 1,392 (33) 1,798 (41) 2,400 (36) 3,116 (65) 3

Within-person mean . . . . . . . . . . . . . . . . . . . . . 2,455 (38) 1,258 (34) 1,483 (29) 1,865 (37) 2,360 (35) 2,916 (62) 3

National Cancer Institute Method . . . . . . . . . . . 2,463 (39) 1,522 (43) 1,696 (39) 2,014 (33) 2,412 (35) 2,856 (53) 3

60 and over 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,167 (27) 980 (31) 1,186 (35) 1,629 (32) 2,077 (31) 2,631 (48) 3

Within-person mean . . . . . . . . . . . . . . . . . . . . . 2,140 (25) 1,111 (39) 1,318 (40) 1,671 (21) 2,093 (26) 2,564 (41) 3

National Cancer Institute Method . . . . . . . . . . . 2,147 (25) 1,344 (41) 1,497 (34) 1,775 (25) 2,113 (22) 2,482 (37) 2

Females 

2–5 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,459 (24) 745 (27) 842 (33) 1,096 (23) 1,415 (28) 1,771 (37) 2

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,453 (22) 819 (21) 921 (25) 1,150 (23) 1,401 (22) 1,708 (43) 1

National Cancer Institute Method . . . . . . . . . . . 1,446 (26) 1,011 (30) 1,096 (26) 1,247 (22) 1,429 (25) 1,628 (37) 1

6–11 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,833 (26) 958 (44) 1,133 (21) 1,406 (33) 1,759 (37) 2,192 (44) 2

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,816 (25) 1,078 (33) 1,205 (33) 1,460 (36) 1,786 (20) 2,046 (35) 2

National Cancer Institute Method . . . . . . . . . . . 1,815 (22) 1,366 (66) 1,457 (57) 1,613 (39) 1,801 (23) 2,000 (31) 2

12–19 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,749 (31) 712 (40) 923 (42) 1,220 (28) 1,647 (41) 2,196 (47) 2

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,704 (25) 802 (46) 974 (37) 1,257 (36) 1,671 (36) 2,110 (43) 2

National Cancer Institute Method . . . . . . . . . . . 1,706 (24) 1,097 (44) 1,218 (38) 1,430 (30) 1,686 (24) 1,959 (28) 2

20–39 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,966 (22) 830 (38) 1,041 (31) 1,398 (25) 1,830 (31) 2,404 (24) 3

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,880 (20) 906 (35) 1,097 (41) 1,427 (22) 1,809 (27) 2,238 (29) 2

National Cancer Institute Method . . . . . . . . . . . 1,883 (21) 1,207 (47) 1,333 (41) 1,564 (31) 1,849 (22) 2,164 (28) 2

40–59 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,824 (18) 805 (22) 962 (25) 1,317 (31) 1,749 (27) 2,200 (34) 2

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,796 (18) 893 (36) 1,050 (29) 1,391 (22) 1,727 (25) 2,137 (32) 2

National Cancer Institute Method . . . . . . . . . . . 1,797 (18) 1,097 (28) 1,232 (25) 1,473 (20) 1,767 (18) 2,089 (25) 2

60 and over 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,637 (24) 720 (40) 902 (25) 1,208 (19) 1,562 (26) 1,961 (39) 2

Within-person mean . . . . . . . . . . . . . . . . . . . . . 1,628 (19) 821 (26) 975 (22) 1,255 (21) 1,597 (23) 1,936 (30) 2

National Cancer Institute Method . . . . . . . . . . . 1,631 (17) 982 (23) 1,103 (21) 1,324 (18) 1,597 (17) 1,902 (24) 2

NOTES: Given day estimates are from Day 1 dietary recall, within-person mean estimates are from Days 1 and 2 dietary recalls, and usual intake distributions are estimated using the National Cancer Institute Methmeasures of energy and the tendency of these to underestimate true energy intake are well documented (see references 6,15–17 in the report). Usual intake methods do not address the systematic bias associated (see reference 9 in the report). Children who were reported to consume breast milk and women who were pregnant or lactating were excluded from the data analysis. SOURCE: NCHS, National Health and Nutrition Examination Survey, 2011–2014. 
Table 5. Percentage of energy intake from total fat (per day): Mean, percentile, and percentage of persons meeting dietary reference intakes, calculate

mean, and the National Cancer Institute Method in the U.S. population aged 2 years and over: National Health and Nutrition Examination Survey, 2011–

Sex, age group (years), Mean 5th 10th 25th 50th 75th 90th 

and method (standard error) (standard error) (standard error) (standard error) (standard error) (standard error) (standard error) (

Males 

2–5 

Given day . . . . . . . . . . . . . . . . . . . . . . . 31.7 (0.41) 21.0 (0.63) 22.8 (0.49) 26.2 (0.47) 31.7 (0.53) 36.7 (0.41) 40.6 (0.64) 

Ratio of within-person mean . . . . . . . . . 31.5 (0.37) 21.8 (0.44) 23.6 (0.51) 26.8 (0.47) 31.5 (0.47) 35.8 (0.53) 40.0 (0.65) 

Within-person mean mean ratio . . . . . . 28.9 (0.38) 12.7 (0.96) 16.8 (1.13) 24.0 (0.63) 30.0 (0.44) 34.5 (0.50) 38.9 (0.76) 

Ratio of National Cancer Institute 

Method . . . . . . . . . . . . . . . . . . . . . . . . 32.0 (0.47) 25.3 (0.38) 26.7 (0.28) 29.2 (0.27) 31.9 (0.45) 34.8 (0.75) 37.2 (1.01) 

National Cancer Institute Method 

mean ratio. . . . . . . . . . . . . . . . . . . . . . 31.5 (0.36) 24.6 (0.49) 26.1 (0.37) 28.7 (0.28) 31.5 (0.38) 34.3 (0.58) 36.8 (0.77) 

6–11 

Given day . . . . . . . . . . . . . . . . . . . . . . . 32.7 (0.26) 20.8 (0.73) 23.5 (0.59) 28.4 (0.51) 32.9 (0.37) 37.3 (0.58) 41.6 (0.67) Ratio of within-person mean . . . . . . . . . 32.5 (0.26) 23.5 (0.52) 25.3 (0.42) 28.7 (0.36) 32.5 (0.41) 36.3 (0.44) 40.0 (0.36) Within-person mean mean ratio . . . . . . 30.1 (0.34) 14.7 (0.73) 18.1 (1.06) 26.0 (0.57) 31.2 (0.40) 35.4 (0.45) 38.6 (0.46) Ratio of National Cancer Institute 

Method . . . . . . . . . . . . . . . . . . . . . . . . 33.0 (0.27) 27.4 (0.80) 28.8 (0.61) 30.8 (0.42) 33.0 (0.28) 35.3 (0.37) 37.2 (0.59) 

National Cancer Institute Method 

mean ratio. . . . . . . . . . . . . . . . . . . . . . 32.4 (0.27) 27.0 (0.81) 28.2 (0.66) 30.2 (0.42) 32.4 (0.27) 34.6 (0.38) 36.5 (0.58) 

12–19 

Given day . . . . . . . . . . . . . . . . . . . . . . . 33.2 (0.37) 20.6 (0.59) 23.3 (0.75) 27.8 (0.54) 33.7 (0.51) 38.2 (0.55) 42.9 (0.51) Ratio of within-person mean . . . . . . . . . 33.3 (0.30) 23.0 (0.74) 25.8 (0.39) 29.5 (0.39) 33.4 (0.40) 37.6 (0.30) 40.7 (0.26) 

Within-person mean mean ratio . . . . . . 31.1 (0.34) 15.9 (1.07) 19.1 (0.86) 27.1 (0.52) 32.3 (0.50) 36.5 (0.52) 40.2 (0.41) Ratio of National Cancer Institute 

Method . . . . . . . . . . . . . . . . . . . . . . . . 33.5 (0.42) 27.9 (0.77) 29.2 (0.59) 31.2 (0.45) 33.5 (0.42) 35.8 (0.57) 37.7 (0.77) 

National Cancer Institute Method 

mean ratio. . . . . . . . . . . . . . . . . . . . . . 33.2 (0.27) 27.8 (0.81) 29.0 (0.64) 31.0 (0.40) 33.2 (0.27) 35.4 (0.42) 37.3 (0.65) 

20–39 

Given day . . . . . . . . . . . . . . . . . . . . . . . 33.1 (0.30) 18.4 (0.76) 22.0 (0.62) 27.6 (0.32) 33.4 (0.35) 38.4 (0.32) 43.8 (0.45) Ratio of within-person mean . . . . . . . . . 33.3 (0.27) 21.3 (0.71) 24.4 (0.54) 28.7 (0.28) 33.2 (0.39) 37.9 (0.28) 42.6 (0.27) 

Within-person mean mean ratio . . . . . . 30.1 (0.41) 13.8 (0.95) 16.7 (0.65) 24.4 (0.74) 31.0 (0.41) 36.4 (0.41) 41.3 (0.53) 

Ratio of National Cancer Institute 

Method . . . . . . . . . . . . . . . . . . . . . . . . 33.7 (0.30) 26.8 (0.65) 28.3 (0.57) 30.8 (0.44) 33.7 (0.33) 36.5 (0.40) 39.1 (0.58) 

National Cancer Institute Method 

mean ratio. . . . . . . . . . . . . . . . . . . . . . 33.1 (0.24) 25.8 (0.69) 27.4 (0.54) 30.1 (0.31) 33.1 (0.25) 36.0 (0.46) 38.6 (0.72) 

40–59 

Given day . . . . . . . . . . . . . . . . . . . . . . . 33.7 (0.32) 19.1 (0.68) 21.8 (0.49) 28.0 (0.48) 33.9 (0.32) 39.7 (0.43) 44.3 (0.57) 

Ratio of within-person mean . . . . . . . . . 34.0 (0.27) 21.2 (0.62) 24.1 (0.44) 29.6 (0.35) 34.3 (0.32) 38.7 (0.23) 42.9 (0.53) 

Within-person mean mean ratio . . . . . . 32.4 (0.33) 16.4 (0.58) 19.9 (0.60) 27.6 (0.52) 33.1 (0.37) 38.0 (0.29) 42.3 (0.54) 

Ratio of National Cancer Institute 

Method . . . . . . . . . . . . . . . . . . . . . . . . 34.2 (0.46) 27.2 (0.62) 28.7 (0.52) 31.3 (0.42) 34.2 (0.45) 37.1 (0.65) 39.7 (0.99) 

National Cancer Institute Method 

mean ratio. . . . . . . . . . . . . . . . . . . . . . 33.8 (0.23) 26.4 (0.57) 28.1 (0.45) 30.8 (0.28) 33.9 (0.23) 36.9 (0.37) 39.6 (0.55) 

See footnotes at end of table. 
Table 5. Percentage of energy intake from total fat (per day): Mean, percentile, and percentage of persons meeting dietary reference intakes, calculate

mean, and the National Cancer Institute Method in the U.S. population aged 2 years and over: National Health and Nutrition Examination Survey, 2011–

Sex, age group (years), Mean 5th 10th 25th 50th 75th 90th 

and method (standard error) (standard error) (standard error) (standard error) (standard error) (standard error) (standard error) (

Males—Con. 

60 and over 

Given day . . . . . . . . . . . . . . . . . . . . . . . 34.3 (0.34) 18.6 (1.03) 23.7 (0.69) 28.7 (0.26) 34.1 (0.46) 39.4 (0.36) 45.9 (0.75) 

Ratio of within-person mean . . . . . . . . . 34.3 (0.29) 21.9 (0.58) 25.3 (0.59) 29.2 (0.31) 34.3 (0.43) 39.0 (0.37) 43.4 (0.59) 

Within-person mean mean ratio . . . . . . 32.8 (0.32) 17.4 (0.81) 21.0 (0.69) 27.8 (0.37) 33.5 (0.49) 38.2 (0.35) 42.5 (0.60) 

Ratio of National Cancer Institute 

Method . . . . . . . . . . . . . . . . . . . . . . . . 34.8 (0.32) 26.1 (0.57) 28.1 (0.48) 31.2 (0.41) 34.8 (0.33) 38.3 (0.36) 41.4 (0.44) 

National Cancer Institute Method 

mean ratio. . . . . . . . . . . . . . . . . . . . . . 34.0 (0.28) 25.2 (0.50) 27.2 (0.41) 30.5 (0.31) 34.1 (0.28) 37.7 (0.37) 40.8 (0.50) 

Females 

2–5 

Given day . . . . . . . . . . . . . . . . . . . . . . . 32.4 (0.33) 20.9 (1.05) 24.0 (0.50) 27.8 (0.39) 32.5 (0.35) 37.5 (0.44) 40.9 (0.45) 

Ratio of within-person mean . . . . . . . . . 32.0 (0.28) 22.3 (0.49) 24.5 (0.41) 28.5 (0.47) 32.3 (0.37) 36.0 (0.38) 39.1 (0.44) Within-person mean mean ratio . . . . . . 29.5 (0.37) 14.4 (0.90) 17.9 (1.03) 25.3 (0.90) 30.9 (0.47) 34.7 (0.34) 38.3 (0.46) 

Ratio of National Cancer Institute 

Method . . . . . . . . . . . . . . . . . . . . . . . . 32.8 (0.34) 27.1 (0.87) 28.4 (0.68) 30.5 (0.48) 32.9 (0.35) 35.1 (0.45) 37.1 (0.69) 

National Cancer Institute Method 

mean ratio. . . . . . . . . . . . . . . . . . . . . . 32.0 (0.27) 26.5 (0.92) 27.7 (0.72) 29.8 (0.41) 32.0 (0.27) 34.2 (0.49) 36.1 (0.76) 

6–11 

Given day . . . . . . . . . . . . . . . . . . . . . . . 33.1 (0.29) 20.7 (0.60) 23.6 (0.50) 28.6 (0.32) 33.5 (0.49) 37.6 (0.42) 41.8 (0.47) 

Ratio of within-person mean . . . . . . . . . 33.3 (0.23) 23.9 (0.48) 26.4 (0.29) 29.7 (0.30) 33.2 (0.34) 36.9 (0.34) 40.5 (0.60) 

Within-person mean mean ratio . . . . . . 31.3 (0.36) 17.4 (0.75) 21.1 (1.28) 27.4 (0.56) 32.0 (0.40) 36.1 (0.24) 39.6 (0.47) 

Ratio of National Cancer Institute 

Method . . . . . . . . . . . . . . . . . . . . . . . . 33.8 (0.29) 29.4 (0.71) 30.4 (0.53) 32.0 (0.33) 33.8 (0.29) 35.5 (0.44) 37.2 (0.61) 

National Cancer Institute Method 

mean ratio. . . . . . . . . . . . . . . . . . . . . . 33.0 (0.26) 28.3 (0.62) 29.4 (0.48) 31.1 (0.29) 33.0 (0.26) 34.9 (0.43) 36.6 (0.64) 

12–19 

Given day . . . . . . . . . . . . . . . . . . . . . . . 33.0 (0.50) 18.0 (2.60) 22.5 (1.09) 28.3 (0.58) 33.3 (0.46) 38.7 (0.49) 43.4 (0.61) 

Ratio of within-person mean . . . . . . . . . 33.2 (0.28) 21.2 (1.18) 24.8 (0.74) 29.4 (0.45) 33.7 (0.40) 37.5 (0.26) 41.4 (0.41) 

Within-person mean mean ratio . . . . . . 31.3 (0.38) 14.8 (0.97) 18.8 (0.98) 27.3 (0.72) 32.5 (0.43) 36.9 (0.40) 40.7 (0.51) 

Ratio of National Cancer Institute 

Method . . . . . . . . . . . . . . . . . . . . . . . . 33.8 (0.51) 28.5 (1.70) 29.7 (1.28) 31.7 (0.76) 33.9 (0.47) 35.9 (0.66) 37.7 (0.97) 

National Cancer Institute Method 

mean ratio. . . . . . . . . . . . . . . . . . . . . . 32.9 (0.34) 26.5 (1.54) 28.0 (1.19) 30.4 (0.68) 33.0 (0.33) 35.5 (0.62) 37.6 (1.05) 

20–39 

Given day . . . . . . . . . . . . . . . . . . . . . . . 33.4 (0.27) 17.6 (0.70) 21.5 (0.41) 28.0 (0.35) 33.4 (0.30) 39.3 (0.44) 43.8 (0.44) 

Ratio of within-person mean . . . . . . . . . 33.2 (0.24) 20.6 (0.55) 23.8 (0.39) 28.5 (0.22) 33.0 (0.24) 37.8 (0.27) 42.6 (0.48) 

Within-person mean mean ratio . . . . . . 30.8 (0.33) 15.4 (0.66) 18.3 (0.66) 25.7 (0.41) 31.3 (0.33) 36.6 (0.28) 41.4 (0.44) 

Ratio of National Cancer Institute 

Method . . . . . . . . . . . . . . . . . . . . . . . . 34.0 (0.24) 26.7 (0.67) 28.3 (0.55) 31.0 (0.38) 34.0 (0.25) 36.9 (0.33) 39.7 (0.51) 

National Cancer Institute Method 

mean ratio. . . . . . . . . . . . . . . . . . . . . . 32.9 (0.24) 25.7 (0.60) 27.3 (0.49) 29.9 (0.33) 32.8 (0.24) 35.8 (0.32) 38.4 (0.49) 

See footnotes at end of table. 
Table 5. Percentage of energy intake from total fat (per day): Mean, percentile, and percentage of persons meeting dietary reference intakes, calculate

mean, and the National Cancer Institute Method in the U.S. population aged 2 years and over: National Health and Nutrition Examination Survey, 2011–

Sex, age group (years), Mean 5th 10th 25th 50th 75th 90th 

and method (standard error) (standard error) (standard error) (standard error) (standard error) (standard error) (standard error) (

Females—Con. 

40–59 

Given day . . . . . . . . . . . . . . . . . . . . . . . 33.6 (0.28) 17.1 (0.96) 21.9 (0.63) 27.7 (0.34) 33.8 (0.51) 40.1 (0.44) 45.0 (0.50) 

Ratio of within-person mean . . . . . . . . . 34.1 (0.24) 21.8 (0.55) 24.7 (0.46) 29.3 (0.43) 34.5 (0.32) 38.9 (0.36) 42.9 (0.38) 

Within-person mean mean ratio . . . . . . 32.8 (0.30) 17.5 (0.61) 21.7 (0.72) 27.5 (0.36) 33.5 (0.33) 38.4 (0.44) 42.5 (0.35) 

Ratio of National Cancer Institute 

Method . . . . . . . . . . . . . . . . . . . . . . . . 34.4 (0.24) 26.8 (0.55) 28.5 (0.44) 31.2 (0.35) 34.3 (0.24) 37.5 (0.30) 40.0 (0.43) 

National Cancer Institute Method 

mean ratio. . . . . . . . . . . . . . . . . . . . . . 33.8 (0.25) 26.0 (0.58) 27.7 (0.48) 30.6 (0.33) 33.8 (0.25) 37.0 (0.31) 39.8 (0.45) 

60 and over 

Given day . . . . . . . . . . . . . . . . . . . . . . . 34.1 (0.37) 18.8 (0.59) 22.3 (0.40) 28.3 (0.45) 34.2 (0.45) 40.4 (0.46) 45.4 (0.57) 

Ratio of within-person mean . . . . . . . . . 34.3 (0.27) 21.8 (0.52) 24.6 (0.38) 29.6 (0.31) 34.4 (0.37) 39.4 (0.38) 43.8 (0.40) 

Within-person mean mean ratio . . . . . . 33.2 (0.34) 18.3 (0.81) 22.3 (0.62) 28.3 (0.44) 33.7 (0.37) 38.4 (0.41) 43.2 (0.48) 

Ratio of National Cancer Institute 

Method . . . . . . . . . . . . . . . . . . . . . . . . 34.9 (0.30) 26.9 (0.85) 28.7 (0.66) 31.6 (0.44) 34.9 (0.30) 38.2 (0.47) 41.0 (0.72) 

National Cancer Institute Method 

mean ratio. . . . . . . . . . . . . . . . . . . . . . 34.1 (0.24) 26.2 (0.72) 27.9 (0.56) 30.8 (0.32) 34.0 (0.24) 37.3 (0.46) 40.3 (0.72) 

 --- Data not available for bivariate macros (ratio of usual intake). The ratio of usual intake is run with bivariate usual intake macros, not the mixtran and distrib for usual ratio. The bivariate macros can only calculate percentage below the lower cut point and the percentage below the upper cut point). To find the percentage within acceptable macronutrient distribution range (AMDR), simple subtraction is used, which is a differenratio, where the percentage within AMDR is actually estimated in the macro. NOTES: AMDR is acceptable macronutrient distribution range. Given day estimates are from Day 1 dietary recall, within-person mean estimates are from Days 1 and 2 dietary recalls, and usual intake distributions Institute Method. The recommended way to evaluate the adequacy of dietary intake in relation to reference standards is by using methods that estimate usual intake and therefore account for within-person variationday and within-person mean with reference standards is not recommended. They are presented to illustrate the inaccuracy associated with using methods that do not account for within-person variation (see referenwere reported to consume breast milk and women who were pregnant or lactating were excluded from the data analysis. SOURCE: NCHS, National Health and Nutrition Examination Survey, 2011–2014. 
Page 26  Series 2, No. 178 

Table 6.Total fruit (cup equivalents per day): Mean, percentile, and the percentage of persons below and above the 2015–2020 Dietary 

Guidelines recommended consumption range, calculated by given day, within-person mean, and the National Cancer Institute Method in 

the U.S. population aged 2 years and over: National Health and Nutrition Examination Survey, 2009–2012 

Sex, age group (years), Mean 5th 10th 25th 50th 

and method (standard error) (standard error) (standard error) (standard error) (standard error) 

Males 

2–5 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 (0.06) --­ --­ 0.0 (0.02) 0.5 (0.07) 1.2 (0.09) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 (0.05) 0.1 (0.02) 0.2 (0.03) 0.7 (0.06) 1.3 (0.06) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.5 (0.06) 0.5 (0.06) 0.7 (0.06) 1.0 (0.06) 1.4 (0.06) 

6–11 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 (0.06) --­ --­ --­ --­ 0.1 (0.04) 0.8 (0.08) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 (0.05) --­ --­ 0.0 (0.02) 0.4 (0.05) 1.0 (0.04) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.2 (0.05) 0.4 (0.07) 0.6 (0.07) 0.8 (0.07) 1.1 (0.06) 

12–19 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 (0.11) --­ --­ --­ --­ --­ --­ 0.5 (0.14) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 (0.08) --­ --­ --­ --­ 0.1 (0.03) 0.6 (0.07) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.0 (0.07) 0.2 (0.03) 0.2 (0.04) 0.5 (0.05) 0.8 (0.06) 

20–39 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.0 (0.05) --­ --­ --­ --­ --­ --­ 0.3 (0.05) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.0 (0.05) --­ --­ --­ --­ --­ --­ 0.6 (0.05) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.0 (0.04) 0.1 (0.02) 0.2 (0.03) 0.4 (0.04) 0.8 (0.04) 

40–59 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.0 (0.04) --­ --­ --­ --­ --­ --­ 0.5 (0.08) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 (0.04) --­ --­ --­ --­ 0.0 (0.00) 0.7 (0.10) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.1 (0.04) 0.1 (0.03) 0.2 (0.03) 0.5 (0.04) 0.9 (0.05) 

60 and over 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 (0.06) --­ --­ --­ --­ --­ --­ 0.8 (0.05) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 (0.05) --­ --­ --­ --­ 0.3 (0.04) 0.9 (0.05) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.2 (0.05) 0.2 (0.03) 0.3 (0.03) 0.6 (0.03) 1.0 (0.05) 

Females 

2–5 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 (0.07) --­ --­ 0.1 (0.03) 0.5 (0.05) 1.1 (0.07) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 (0.07) 0.1 (0.05) 0.3 (0.06) 0.7 (0.06) 1.3 (0.07) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.5 (0.08) 0.5 (0.07) 0.7 (0.06) 1.0 (0.06) 1.4 (0.07) 

6–11 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 (0.06) --­ --­ --­ --­ 0.2 (0.08) 0.9 (0.10) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 (0.07) --­ --­ 0.1 (0.03) 0.5 (0.04) 1.0 (0.07) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.2 (0.07) 0.5 (0.08) 0.6 (0.07) 0.9 (0.07) 1.1 (0.07) 

12–19 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.8 (0.05) --­ --­ --­ --­ --­ --­ 0.3 (0.07) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 0.8 (0.04) --­ --­ --­ --­ 0.1 (0.02) 0.6 (0.06) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 0.8 (0.04) 0.2 (0.04) 0.3 (0.04) 0.4 (0.04) 0.7 (0.04) 

20–39 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.9 (0.05) --­ --­ --­ --­ --­ --­ 0.4 (0.07) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 0.9 (0.04) --­ --­ --­ --­ 0.1 (0.02) 0.6 (0.04) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 0.9 (0.04) 0.2 (0.03) 0.3 (0.03) 0.5 (0.04) 0.8 (0.04) 

40–59 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.0 (0.04) --­ --­ --­ --­ --­ --­ 0.6 (0.05) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.0 (0.04) --­ --­ --­ --­ 0.2 (0.04) 0.7 (0.05) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.0 (0.04) 0.2 (0.02) 0.3 (0.03) 0.5 (0.03) 0.9 (0.03) 

60 and over 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 (0.04) --­ --­ --­ --­ 0.1 (0.03) 0.8 (0.03) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 (0.03) --­ --­ 0.0 (0.01) 0.4 (0.03) 1.0 (0.03) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.2 (0.04) 0.3 (0.03) 0.4 (0.03) 0.7 (0.03) 1.0 (0.04) 

See footnotes at end of table. 
Series 2, No. 178  Page 27 

Table 6. Total fruit (cup equivalents per day): Mean, percentile, and the percentage of persons below and above the 2015–2020 Dietary 

Guidelines recommended consumption range, calculated by given day, within-person mean, and the National Cancer Institute Method in 

the U.S. population aged 2 years and over: National Health and Nutrition Examination Survey, 2009–2012—Con. 

Percent Percent 

Sex, age group (years), 75th 90th 95th below range above range 

and method (standard error) (standard error) (standard error) (standard error) (standard error) 

Males 

2–5 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 (0.14) 3.3 (0.16) 4.2 (0.22) 35.2 (3.15) 33.9 (2.76) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 (0.13) 3.0 (0.13) 3.6 (0.20) 34.7 (2.40) 34.3 (3.20) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.9 (0.09) 2.5 (0.14) 2.8 (0.18) 27.1 (3.43) 33.2 (3.43) 

6–11 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.8 (0.08) 2.8 (0.10) 3.5 (0.18) 42.5 (2.31) 20.5 (1.79) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.8 (0.09) 2.5 (0.12) 3.2 (0.15) 51.8 (2.20) 18.9 (1.90) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.5 (0.06) 1.9 (0.10) 2.2 (0.14) 57.0 (3.71) 8.9 (2.10) 

12–19 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 (0.15) 3.0 (0.26) 3.9 (0.33) 45.1 (2.61) 15.2 (2.17) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 (0.11) 2.6 (0.28) 3.7 (0.20) 63.2 (1.90) 12.6 (1.90) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.4 (0.09) 2.1 (0.15) 2.6 (0.20) 86.1 (2.26) 7.3 (1.62) 

20–39 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 (0.09) 3.1 (0.18) 4.1 (0.18) 45.0 (1.72) 13.8 (1.12) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 (0.08) 2.6 (0.15) 3.4 (0.24) 60.8 (1.90) 10.7 (1.20) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.3 (0.06) 2.0 (0.10) 2.5 (0.14) 90.0 (1.38) 5.0 (0.99) 

40–59 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 (0.09) 2.7 (0.15) 3.8 (0.21) 48.7 (2.36) 11.3 (1.16) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 (0.08) 2.7 (0.09) 3.4 (0.18) 62.4 (2.00) 12.3 (1.10) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.4 (0.05) 2.1 (0.08) 2.7 (0.13) 88.0 (1.18) 6.2 (0.94) 

60 and over 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.8 (0.09) 3.0 (0.12) 3.6 (0.18) 53.9 (1.94) 14.1 (1.43) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 (0.09) 2.7 (0.10) 3.3 (0.13) 66.0 (2.20) 13.4 (1.40) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.6 (0.08) 2.2 (0.12) 2.7 (0.15) 85.9 (2.08) 6.8 (1.41) 

Females 

2–5 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.0 (0.12) 2.9 (0.12) 3.4 (0.26) 38.9 (2.09) 39.4 (1.92) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 2.0 (0.11) 3.1 (0.21) 3.7 (0.28) 34.2 (2.70) 41.3 (2.50) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.9 (0.10) 2.4 (0.15) 2.8 (0.19) 27.2 (3.65) 43.3 (4.08) 

6–11 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.8 (0.13) 2.8 (0.08) 3.2 (0.13) 43.8 (2.09) 27.5 (2.35) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.8 (0.10) 2.5 (0.13) 3.1 (0.24) 52.5 (2.90) 25.8 (3.00) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.5 (0.09) 1.9 (0.12) 2.1 (0.16) 57.0 (5.29) 15.3 (3.88) 

12–19 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 (0.08) 2.2 (0.16) 2.7 (0.18) 44.8 (3.43) 12.1 (1.74) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 (0.07) 1.8 (0.09) 2.3 (0.13) 64.3 (2.70) 8.5 (1.10) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.0 (0.05) 1.4 (0.10) 1.7 (0.14) 91.3 (2.30) 2.4 (1.22) 

20–39 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 (0.07) 2.5 (0.14) 3.4 (0.21) 44.3 (1.20) 16.4 (1.26) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 (0.07) 2.4 (0.11) 3.2 (0.15) 58.8 (1.60) 14.5 (1.30) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.2 (0.05) 1.8 (0.08) 2.1 (0.11) 83.9 (1.67) 6.4 (1.20) 

40–59 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 (0.09) 2.5 (0.15) 3.3 (0.23) 49.6 (1.85) 16.3 (1.23) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 (0.06) 2.4 (0.06) 3.1 (0.13) 61.5 (1.60) 15.5 (1.00) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.4 (0.05) 1.9 (0.10) 2.4 (0.14) 79.7 (1.90) 9.2 (1.51) 

60 and over 

Given day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 (0.05) 2.6 (0.09) 3.1 (0.12) 56.0 (1.48) 18.3 (1.16) 

Within-person mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 (0.06) 2.4 (0.09) 3.1 (0.09) 63.5 (2.00) 16.5 (1.20) 

National Cancer Institute Method . . . . . . . . . . . . . . . . 1.5 (0.05) 2.1 (0.08) 2.4 (0.10) 73.9 (2.14) 11.0 (1.48) 

0.0 Quantity more than zero but less than 0.05. 

--- SUDAAN is unable to estimate any quantile less than or equal to the percentage of data accounted for by the 0 values. 

NOTES: Given day estimates are from Day 1 dietary recall, within-person mean estimates are from Days 1 and 2 dietary recalls, and usual intake distributions are estimated using the National Cancer 

Institute Method. The recommended way to evaluate the adequacy of dietary intake in relation to reference standards is by using methods that estimate usual intake and therefore account for 

within-person variation. Comparing estimates based on given day and within-person mean with reference standards is not recommended. They are presented to illustrate the inaccuracy associated 

with using methods that do not account for within-person variation (see references 12–15 in the report). Children who were reported to consume breast milk and women who were pregnant or 

lactating were excluded from the data analysis. 

SOURCE: NCHS, National Health and Nutrition Examination Survey, 2009–2012. 
Page 28  Series 2, No. 178 

Appendix I. Nutrient Reference Values Used in This Report 

Table I. Dietary reference intake for calcium (estimated average requirement and tolerable upper limit), percentange of energy from fat 

(acceptable macronutrient distribution range), and recommended intake range of fruit, by the Institute of Medicine Life Stage Group 

Sex and life stage 

age group (years)1 

Estimated average 

requirement1 for calcium 

(milligrams per day) 

Tolerable 

upper limit1 for calcium 

(milligrams per day) 

Acceptable macronutrient 

distribution range1 for

 percent of energy from fat 

Recommended 

intake of fruit2 

(cup equivalents per day) 

Males 

1–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . 700 2,500 30–40 1.0–1.5 

4–8 . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,000 2,500 25–35 1.0–2.0 

9–13 . . . . . . . . . . . . . . . . . . . . . . . . . . 1,300 3,000 25–35 1.5–2.0 

14–18 . . . . . . . . . . . . . . . . . . . . . . . . . 1,300 3,000 25–35 2.0–2.5 

19–30 . . . . . . . . . . . . . . . . . . . . . . . . . 1,000 2,500 20–35 2.0–2.5 

31–50 . . . . . . . . . . . . . . . . . . . . . . . . . 1,000 2,500 20–35 2.0–2.5 

51–70 . . . . . . . . . . . . . . . . . . . . . . . . . 1,000 2,500 20–35 2.0–2.5 

70 and over. . . . . . . . . . . . . . . . . . . . . 1,200 2,000 20–35 2.0–2.5 

Females 

1–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . 700 2,500 30–40 1.0–1.5 

4–8 . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,000 2,500 25–35 1.0–1.5 

9–13 . . . . . . . . . . . . . . . . . . . . . . . . . . 1,300 3,000 25–35 1.5–2.0 

14–18 . . . . . . . . . . . . . . . . . . . . . . . . . 1,300 3,000 25–35 1.5–2.0 

19–30 . . . . . . . . . . . . . . . . . . . . . . . . . 1,000 2,500 20–35 1.5–2.0 

31–50 . . . . . . . . . . . . . . . . . . . . . . . . . 1,000 2,500 20–35 1.5–2.0 

51–70 . . . . . . . . . . . . . . . . . . . . . . . . . 1,200 2,000 20–35 1.5–2.0 

70 and over. . . . . . . . . . . . . . . . . . . . . 1,200 2,000 20–35 1.5–2.0 

1

Institute of Medicine. Dietary reference intakes: The essential guide to nutrient requirements. Washington, DC: The National Academies Press. 2006. 

2

U.S. Department of Health and Human Services and U.S. Department of Agriculture. Dietary guidelines for Americans 2015–2020. 8th ed. 2015. 
Series 2, No. 178  Page 29 

Appendix II. Steps for Creating Poststratified Balanced Repeated 

Replication Weights 

Key Notes e. Merge new expansion factor 

variable into the original data set 

● Must use full sample with BRR weights (by PScell) 

● Use PROC SURVEYFREQ f. Calculate NEW rounded 

to calculate balanced repeated poststratified weight 

replication (BRR) weights (PSWEIGHT_i) by multiplying 

● Poststratify BRR weights to match the BRR weight by the expansion 

original weighting factor (or ratio) 

g. Repeat steps b–f for each RepWt 

Steps h. Run steps b–f for original weight 

variable (wtdrd1) 

1. Import data set into SAS where all 8. Check to ensure that all 

respondents have a weight greater poststratified BRR weights are 

than zero (Day 1 data only). equivalent to original weighted 

2. Define the poststratification cells: frequency counts (control). 

a. Typically sex x race x age 

b. Use age groups defined by 

Dietary Reference Intakes (DRI) 

i. Example: age groups from 

Table I 

3. Calculate the weighted count for 

all possible combinations of sex x 

race x age for the poststratification 

cells (PScell). THESE ARE YOUR 

CONTROL TOTALS. 

4. Merge in Day 2 data. 

5. Create a data set with a maximum of 

two lines of data per person (Days 1 

and 2 diet recalls). 

6. Create BRR weights in SAS. 

7. Run macro for poststratification: 

a. Sort data with BRR weights by 

PScell variable 

b. Output weighted frequency 

counts of the sample data set, 

once for each BRR weight 

c. Merge weighted frequency 

counts of the sample data set 

with original control data set, by 

PScell 

d. Calculate the ratio of the 

weighted control frequency 

counts to weighted frequency 

counts of the sample data set for 

each BRR weight (ctrl/COUNT) 

(by RepWT_i, where i = 1 to the 

number of BRR weights created, 

dependent on the number of 

NHANES survey cycles merged) 

i. This is referred to as the 

expansion factor 
 

 

 

 

 

Page 30  Series 2, No. 178 

Appendix III. SAS Syntax Used to Create Poststratified Adjusted Balanced
	

Repeated Replication Weights for Use in Usual Intake Estimation 

***********************************************************************************; 

Syntax used to create poststratified adjusted balanced repeated replication (BRR) weights for use in Usual 

Intake Estimation by 

the NCI Method 

ALL US persons, 0–150 YRS 2009–2010 

Purpose of syntax: BRR WEIGHT DERIVATION FOR A SINGLE SURVEY OF NHANES, IN THIS 

EXAMPLE 2009–2010 

SUBPOP: ALL US PERSONS (BRR WEIGHTS WILL ONLY BE CREATED IF SP HAS A WEIGHT 

VALUE FOR WTDRD1) 

NOTES: 

To adapt this syntax to additional survey cycles of NHANES analysis, merge in the relevant years and create 

the appropriate number of BRR weights according to the number of cycles of NHANES. 

**********************************************************************************; 

CREATING THE DATA SETS 

DATA: 

NHANES 2009–2010 (0910); 

*paste the path name to the folder where the data files are stored; 

libname f “datafile\path\name\here”; 

*paste the path name to the folder where the BRR output is to be saved; 

libname DataOut “outputBRRweights\path\name\here “; 

*--------------------------------------------------------------------------; 

* Create a data set from the permanent data sets ; 

*--------------------------------------------------------------------------; 

*DEMOGRAPHICS; 

data WORK.demo ; 

set

 F.demo_f (KEEP=SEQN SDDSRVYR RIDSTATR RIAGENDR RIDAGEYR RIDRETH1 

SDMVPSU SDMVSTRA); 

RUN; 

PROC SORT DATA=WORK.DEMO;
 

BY SEQN;
 

RUN;
 
 

 

 

Series 2, No. 178  Page 31 

*DIET DATA - Day one; 

data WORK.DR1TOT (KEEP=SEQN WTDRD1 DR1DRSTZ DR1TCALC DR1DAY) ; 

set F.DR1TOT_F ; 

RUN; 

PROC SORT DATA=WORK.DR1TOT;
 

BY SEQN;
 

RUN;
 

DATA WORK.DR1TOTPLUS ; 

MERGE WORK.DEMO 

WORK.DR1TOT 

BY SEQN; 

*create all possible combinations of poststratification cells by gender x race x age; 

*the age groups are based on Dietary Reference Intakes (DRI) age groups; 

*must collapse some race groups to get stable estimates; 

* USE ALL ages; 

if riagendr=1 and ridreth1 in (1,2,5) and .<ridageyr<1 then PScell=1; 

if riagendr=1 and ridreth1 in (1,2,5) and ridageyr>=1 and ridageyr<4 then PScell=2; 

if riagendr=1 and ridreth1 in (1,2,5) and ridageyr>=4 and ridageyr<9 then PScell=3; 

if riagendr=1 and ridreth1 in (1,2,5) and ridageyr>=9 and ridageyr<14 then PScell=4; 

if riagendr=1 and ridreth1 in (1,2,5) and ridageyr>=14 and ridageyr<19 then PScell=5; 

if riagendr=1 and ridreth1 in (1,2,5) and ridageyr>=19 and ridageyr<31 then PScell=6; 

if riagendr=1 and ridreth1 in (1,2,5) and ridageyr>=31 and ridageyr<51 then PScell=8; 

if riagendr=1 and ridreth1 in (1,2,5) and ridageyr>=51 and ridageyr<70 then PScell=8; 

if riagendr=1 and ridreth1 in (1,2,5) and ridageyr>=70 then PScell=9; 

if riagendr=2 and ridreth1 in (1,2,5) and .<ridageyr<1 then PScell=10; 

if riagendr=2 and ridreth1 in (1,2,5) and ridageyr>=1 and ridageyr<4 then PScell=11; 

if riagendr=2 and ridreth1 in (1,2,5) and ridageyr>=4 and ridageyr<9 then PScell=12; 

if riagendr=2 and ridreth1 in (1,2,5) and ridageyr>=9 and ridageyr<14 then PScell=13; 

if riagendr=2 and ridreth1 in (1,2,5) and ridageyr>=14 and ridageyr<19 then PScell=14; 

if riagendr=2 and ridreth1 in (1,2,5) and ridageyr>=19 and ridageyr<31 then PScell=15; 

if riagendr=2 and ridreth1 in (1,2,5) and ridageyr>=31 and ridageyr<51 then PScell=16; 

if riagendr=2 and ridreth1 in (1,2,5) and ridageyr>=51 and ridageyr<70 then PScell=17; 

if riagendr=2 and ridreth1 in (1,2,5) and ridageyr>=70 then PScell=18; 
 

Page 32  Series 2, No. 178 

if riagendr=1 and ridreth1 in (3) and .<ridageyr<1 then PScell=19;
 

if riagendr=1 and ridreth1 in (3) and ridageyr>=1 and ridageyr<4 then PScell=20;
 

if riagendr=1 and ridreth1 in (3) and ridageyr>=4 and ridageyr<9 then PScell=21;
 

if riagendr=1 and ridreth1 in (3) and ridageyr>=9 and ridageyr<14 then PScell=22;
 

if riagendr=1 and ridreth1 in (3) and ridageyr>=14 and ridageyr<19 then PScell=23;
 

if riagendr=1 and ridreth1 in (3) and ridageyr>=19 and ridageyr<31 then PScell=24;
 

if riagendr=1 and ridreth1 in (3) and ridageyr>=31 and ridageyr<51 then PScell=25;
 

if riagendr=1 and ridreth1 in (3) and ridageyr>=51 and ridageyr<70 then PScell=26;
 

if riagendr=1 and ridreth1 in (3) and ridageyr>=70 then PScell=27;
 

if riagendr=2 and ridreth1 in (3) and .<ridageyr<1 then PScell=28;
 

if riagendr=2 and ridreth1 in (3) and ridageyr>=1 and ridageyr<4 then PScell=29;
 

if riagendr=2 and ridreth1 in (3) and ridageyr>=4 and ridageyr<9 then PScell=30;
 

if riagendr=2 and ridreth1 in (3) and ridageyr>=9 and ridageyr<14 then PScell=31;
 

if riagendr=2 and ridreth1 in (3) and ridageyr>=14 and ridageyr<19 then PScell=32;
 

if riagendr=2 and ridreth1 in (3) and ridageyr>=19 and ridageyr<31 then PScell=33;
 

if riagendr=2 and ridreth1 in (3) and ridageyr>=31 and ridageyr<51 then PScell=34;
 

if riagendr=2 and ridreth1 in (3) and ridageyr>=51 and ridageyr<70 then PScell=35;
 

if riagendr=2 and ridreth1 in (3) and ridageyr>=70 then PScell=36;
 

if riagendr=1 and ridreth1 in (4) and .<ridageyr<1 then PScell=37;
 

if riagendr=1 and ridreth1 in (4) and ridageyr>=1 and ridageyr<4 then PScell=38;
 

if riagendr=1 and ridreth1 in (4) and ridageyr>=4 and ridageyr<9 then PScell=39;
 

if riagendr=1 and ridreth1 in (4) and ridageyr>=9 and ridageyr<14 then PScell=40;
 

if riagendr=1 and ridreth1 in (4) and ridageyr>=14 and ridageyr<19 then PScell=41;
 

if riagendr=1 and ridreth1 in (4) and ridageyr>=19 and ridageyr<31 then PScell=42;
 

if riagendr=1 and ridreth1 in (4) and ridageyr>=31 and ridageyr<51 then PScell=43;
 

if riagendr=1 and ridreth1 in (4) and ridageyr>=51 and ridageyr<70 then PScell=44;
 

if riagendr=1 and ridreth1 in (4) and ridageyr>=70 then PScell=45;
 

if riagendr=2 and ridreth1 in (4) and .<ridageyr<1 then PScell=46;
 

if riagendr=2 and ridreth1 in (4) and ridageyr>=1 and ridageyr<4 then PScell=47;
 

if riagendr=2 and ridreth1 in (4) and ridageyr>=4 and ridageyr<9 then PScell=48;
 

if riagendr=2 and ridreth1 in (4) and ridageyr>=9 and ridageyr<14 then PScell=49;
 

if riagendr=2 and ridreth1 in (4) and ridageyr>=14 and ridageyr<19 then PScell=50;
 

if riagendr=2 and ridreth1 in (4) and ridageyr>=19 and ridageyr<31 then PScell=51;
 

if riagendr=2 and ridreth1 in (4) and ridageyr>=31 and ridageyr<51 then PScell=52;
 

if riagendr=2 and ridreth1 in (4) and ridageyr>=51 and ridageyr<70 then PScell=53;
 

if riagendr=2 and ridreth1 in (4) and ridageyr>=70 then PScell=54;
 

RUN;
 

PROC FREQ data=dr1totplus;
 

table pscell;
 

title “”;
 

run; *no missing;
 
 

 

 

 

 

 

 

Series 2, No. 178  Page 33 

proc freq data=work.dr1totplus;
 

where ridstatr=2;
 

table dr1drstz;
 

run;
 

proc means data=work.dr1totplus;
 

where ridstatr=2 and dr1drstz in (1, 4);
 

var seqn;
 

title “confirming filters for USDA weight replication”;
 

run; *match N=9754;
 

*-------------------------------------------------------------------------------------------;
 

* create an output data set, called ctrl, that saves the weighted counts ; 

* for each of the poststratification cells; 

* the weighted counts are saved in a variable named ‘ctrl’; 

*-------------------------------------------------------------------------------------------; 

proc freq data= DR1TOTPLUS ; *NOW it is okay to restrict the data to the subpopulation of interest;
 

tables pscell/ out=ctrl(drop=percent rename=(count=ctrl));
 

weight wtdrd1;
 

run;
 

proc print data=ctrl; 

run; 

*-------------------------------------------------------------------------------------------; 

* CHECK FOR stratum with more than 2 PSUs ; 

*-------------------------------------------------------------------------------------------; 

proc freq data=work.DR1TOTPLUS;
 

table SDMVPSU SDMVSTRA;
 

title “categories of PSU AND STATUM”;
 

run; 

PROC FREQ DATA=WORK.DR1TOTPLUS;
 

WHERE SDMVPSU=3;
 

TABLE SDMVSTRA;
 

title “>2 psu’s per stratum check”;
 

RUN; *STRATUM 86;
 

proc sort data=work.DR1TOTPLUS;
 

by seqn;
 

run;
 
 

 

 

 

 

Page 34  Series 2, No. 178 

data work.dietUI; set work.DR1TOTPLUS;
 

if SDMVSTRA=86 and SDMVPSU=3 then SDMVPSU=1; *reassign PSU of stratum 86 to PSU=1;
 

by seqn;
 

if first.seqn and pscell ne . ; *keep first obs per person;
	

run;
 

proc print data=work.dr1totplus (obs=20);
 

where dr1drstz=1;
 

var dr1drstz ridstatr WTDRD1;
 

title “check for weights”;
 

run;
 

proc print data=work.dr1totplus (obs=20);
 

where dr1drstz=2;
 

var dr1drstz ridstatr WTDRD1;
 

title “check for weights”;
 

run;
 

proc print data=work.dr1totplus (obs=20);
 

where dr1drstz=4;
 

var dr1drstz ridstatr WTDRD1;
 

title “check for weights”;
 

run;
 

proc print data=work.dr1totplus (obs=20);
 

where dr1drstz=5;
 

var dr1drstz ridstatr WTDRD1;
 

title “check for weights”;
 

run;
 

proc print data=work.dr1totplus (obs=20);
 

where ridstatr=1;
 

var dr1drstz ridstatr WTDRD1;
 

title “check for weights”;
 

run;
 
 

 

 

Series 2, No. 178  Page 35 

******************************************************************************; 

* Create BRR weights using SAS; 

* NOTE: paper by USDA uses 0.3 for FAY method; 

* 0.3 correlates with a perturbation factor of 70%: 100(1-k), where k=0.3; 

* USDA paper = WWEIA, NHANES 2005–2006. Usual Nutrient Intakes from Food and Water; 

* Compared to 1997 Dietary Reference Intakes for Vitamin D, Calcium, Phosphorus and Magnesium; 

* to create BRR weights appropriate for additional survey cycles – ; 

* CHANGE the value of REPS= ; 

******************************************************************************; 

* This syntax is appropriate for a single survey cycle of NHANES; 

* Each NHANES survey cycle typically has 15 Strata; 

* The number of replicates (R) that are needed for BRR is the smallest integer that is ; 

* divisible by 4 and is greater than or equal to S. For 2 years of NHANES data (a single ; 

* survey cycle), this number is 16; 

******************************************************************************; 

proc surveyfreq data=WORK.DIETUI 

varmethod=BRR(reps=16 outweights=brr_wts FAY=0.3); *paper by USDA uses 0.3 for FAY; 

strata SDMVSTRA; 

cluster SDMVPSU; 

weight wtdrd1; 

tables pscell; 

run; 
 

 

 

 

 

 

 

 

 

Page 36  Series 2, No. 178 

**********************************************************************************; 

*		 Poststratification Macro (pstrat); 

*Definitions: ; 

*numwts = number of BRR weights created (in this example, 16) 

* basename = name of the base weight used to create BRR weights, (WTDRD1 in this example) 

* repname = replicate weight variables (RepWt_ is the name that SAS creates) 

* controldata= data set that has the weighted counts of the full population (created above) 

* psvar = variable that defines the categories of the poststratification combinations (created above) 

* controlvar= variable that that has the weighted counts per category of the poststratification combinations 

(created above) ; 

* data = name of the data set that has the BRR weigthts created by PROC SURVEYFREQ (user defined) 

; 

***********************************************************************************; 

%macro pstrat(numwts=, basename=, repname=RepWt_, controldata=ctrl, psvar=pscell, controlvar=ctrl, 

data=brr_wts);
 

proc sort data=&data;

 by &psvar;

 run; 

%do i=1 %to &numwts; 

proc freq data=&data noprint;

 tables &psvar/out=smpl(drop=percent);

 weight &repname.&i;

 run;

 data smpl;

 merge smpl &controldata;

 by &psvar;

 ratio=&controlvar / count;

 run; 

/* Updated with cumulate and round code */

 data &data;

 retain _c _r -1;

 merge &data smpl(drop=&controlvar count);

 by &psvar;

 psweight_&i=&repname.&i * ratio;

 if first.&psvar then do;

 _c=0;_r=0;

 end;

 _c = _c + psweight_&i;

 psweight_&i=round(_c) - _r;

 _r = _r + psweight_&i;

 drop ratio _c _r;

 run; 

%end; 
 

 

 

 

Series 2, No. 178  Page 37 

proc freq data=&data noprint;

 tables &psvar/out=smpl(drop=percent);

 weight &basename;

 run;

 data smpl;

 merge smpl &controldata;

 by &psvar;

 ratio=&controlvar / count;

 run;

 data &data;

 retain _c _r -1;

 merge &data smpl(drop=&controlvar count);

 by &psvar;

 psweight_0=&basename * ratio;

 if first.&psvar then do;

 _c=0;_r=0;

 end;

 _c = _c + psweight_0;

 psweight_0=round(_c) - _r;

 _r = _r + psweight_0;

 drop ratio _c _r;

 run; 

%mend;
 

%pstrat(numwts=16, basename=wtdrd1);
 

proc freq data=brr_wts;
 

tables pscell;
 

weight psweight_0;
 

run;
 

proc freq data=brr_wts;
 

tables pscell;
 

weight psweight_16;
 

run;
 

data WORK.brr_0910NEW;
 

set brr_wts(keep=SEQN RIDSTATR DR1DRSTZ psweight_0 - psweight_16);
 

run;
 

PROC MEANS DATA=WORK.brr_0910NEW n NMISS MEAN MIN MAX SUM; RUN;
 

PROC SORT DATA=WORK.brr_0910NEW ;
 

BY SEQN;
 

RUN;
 
Page 38  Series 2, No. 178 

data BRR.brr_0910NEW;
 

set WORK.brr_0910NEW;(keep=SEQN RIDSTATR DR1DRSTZ psweight_0 - psweight_16);
 

run;
 

***************************************************************************************
 

*****;
 

* Syntax above has cumulate and round code; 

*************************************************************************************** 

*****; 

* END ; 
Series 2, No. 178  Page 39 

Appendix IV. National Cancer Institute Macros Used to Estimate 

Usual Intake 

Table II. National Cancer Institute macros used to estimate usual intake 

Nutrient and food estimated Method Macros used 

Calcium Ubiquitously consumed–AMOUNT MIXTRAN, DISTRIB, BRR_PVALUE_CI 

Total energy Ubiquitously consumed–AMOUNT MIXTRAN, DISTRIB, BRR_PVALUE_CI 

Total fat Ubiquitously consumed–AMOUNT MIXTRAN, DISTRIB, BRR_PVALUE_CI 

Fruit Episodically consumed–NOCORR MIXTRAN, DISTRIB, BRR_PVALUE_CI 

Percent of energy from fat Usual intake of ratios MIXTRAN, DISTRIB, BRR_PVALUE_CI 

Ratio of usual intakes NLMIXED UNIVARIATE, NLMIXED BIVARIATE, DISTRIB BIVARIATE, 

PERCENTILES_SURVEY 
Page 40  Series 2, No. 178 

Appendix V. Usual Intake Estimation—Univariate–Calcium Example 

/*****************************************************************************/ 

/****************************LIBRARY******************************************/ 

/*****************************************************************************/ 

libname b24 “\\pathtolibrary\”;
 

libname outlib “\\pathtolibrary_where_files_are_to_be_saved”;
 

/*****************************************************************************/ 

/**********************CALL MACROS********************************************/ 

/*****************************************************************************/ 

/*Macros from NCI*/ 

%include “\\pathname\mixtran_macro_v2.1.sas”;
 

%include “\\pathname\distrib_macro_v2.1.sas”;
 

%include “\\pathname\brr_pvalue_ci_macro_v1.0.sas”;
 

%include “\\pathname\percentiles_survey.macro.v1.1.sas”;
 

************************************************************; 

************************************************************; 

************************************************************; 

/*2 macros built to implement UI, run mixtran, distrib, and estimate SEs: nut_UI and brrcalcs*/ 

%macro nut_UI (data, id, outcome, replicfirst, repliclast, replicvar, repeat, ctpts, nctpts, outcome_ear, recco, 

outcome_tul,subgroup); 

%do replicnum = &replicfirst %to &repliclast; 

%PUT “UI &outcome &replicnum”; 

%mixtran(data=&data, response=&outcome, foodtype=&outcome, subject=&id, repeat=&repeat, 

covars_prob=, covars_amt= , 

outlib=outlib, modeltype=amount, lambda=, replicate_var=&replicvar&replicnum, seq=, 

 weekend=weekend, vargroup=, numvargroups=, start_val1=, start_val2=, 

start_val3=, vcontrol=, nloptions=, titles=, printlevel=2, subgroup=&subgroup );
 

run;
 

data outlib._pred_unc_&outcome._&&replicnum;
 

set outlib._pred_unc_&outcome;
 

rep=”&replicnum”;
 

run; 
Series 2, No. 178  Page 41 

data outlib._param_unc_&outcome._&&replicnum;
 

set outlib._param_unc_&outcome;
 

rep=”&replicnum”;
 

run; 

data outlib.etas_&outcome._&&replicnum;
 

set outlib.etas_&outcome;
 

rep=”&replicnum”;
 

run; 

data outlib._parmsf2_&outcome._&&replicnum;
 

set outlib._parmsf2_&outcome;
 

rep=”&replicnum”;
 

run; 

%Distrib (seed=5454768, nsim_mc=1000,modeltype=amount, add_da=&data, 

pred=outlib._pred_unc_&&outcome._&replicnum, param=outlib._param_ 

unc_&&outcome._&replicnum,outlib=outlib,

 cutpoints=&ctpts , recamt=&outcome_ear,recamt_co=&recco,recamt_hi=&outcome_tul, 

ncutpnt=&nctpts, byvar=, subject=seqn,titles=4,food=&outcome, subgroup=&subgroup); 

run;
 

data outlib.brr_&&outcome._&replicvar&replicnum;
 

set outlib.descript_&&outcome._&replicvar&replicnum;
 

run=&replicnum;
 

run;
 

data outlib.brr_&outcome;
 

set outlib.brr_&&outcome._&replicvar&replicfirst-outlib.brr_&&outcome._&replicvar&repliclast; 

run; 

%end; 

%MEND nut_UI; 

************************************************************; 

************************************************************; 

************************************************************; 

%macro brrcalcs(outcome, paramnames); 

%let param_names1= ¶mnames; 

data origwt_paramest_&outcome brr_paramest_&outcome;

 set outlib.brr_&outcome;

 if run=0 then output origwt_paramest_&outcome;

 else output brr_paramest_&outcome; 

run; 
 

 

 

 

 

 

 

 

Page 42  Series 2, No. 178 

title4 “Mixtran and Distrib BRR Means and SEs - Dietary Component:&outcome”; 

%brr_pvalue_ci(data_orig_estimates_1rec = origwt_paramest_&outcome, 

data_brr_estimates_brecs = brr_paramest_&outcome,

 param_estimate_names = ¶m_names1, 

set_f_method_fay = 0.3, 

set_confidence_level = 0.95, 

set_degrees_freedom = 31, 

print = y, 

titles = 4

 ); 

%mend brrcalcs; 

************************************************************; 

************************************************************; 

************************************************************; 

/*process data to generate ‘data file’ here*/ 

* 

; 

/*run mixtran, distrib and replicate*/ 

%nut_UI (data=datafile,outcome=calcium,id=seqn, repeat=daycode,

 replicfirst=0,repliclast=32,replicvar=psweight_,ctpts=, 

 nctpts=, outcome_ear=cal_hi,recco=GT,outcome_tul=,subgroup=); 

*************call BRR macro to get SEs; 

%brrcalcs(outcome=calcium, paramnames=mean_mc_t percent_rec_amt tpercentile5 tpercentile25 tpercentile50 tpercentile75 tpercentile95); 
 

 

Series 2, No. 178  Page 43 

Appendix VI. Usual Intake Estimation—Ratio and Bivariate–Percentage 

of Energy From Fat Example 

/*****************************************************************************/ 

/****************************LIBRARY******************************************/ 

/*****************************************************************************/ 

libname b24 “\\pathtolibrary\”;
 

libname outlib “\\pathtolibrary_where_files_are_to_be_saved”;
 

/*****************************************************************************/ 

/**********************CALL MACROS********************************************/ 

/*****************************************************************************/ 

/*Macros from NCI*/ 

%include “\\pathname\boxcox_survey.macro.v1.1.sas”;
 

%include “\\pathname\std_cov_boxcox24hr_conday_minamt_macro_v1.0.sas”;
 

%include “\\pathname\nlmixed_bivariate.macro.v1.1.sas”;
 

%include “\\pathname\nlmixed_univariate.macro.v1.1.sas”;
 

%include “\\pathname\distrib_bivariate.macro.v1.1.sas”;
 

%include “\\pathname\brr_pvalue_ci_macro_v1.0.sas”;
 

%include “\\pathname\percentiles_survey.macro.v1.1.sas”;
 

************************************************************; 

************************************************************; 

************************************************************; 

/*5 macros built to implement UI, run mixtran, distrib, and estimate SEs: nut_UI and brrcalcs*/ 

/*These 5 macros can be saved in separate SAS programs and called as NCI macros above*/ 

/*1: rununi runs the univariate models for the numerator and denominator of a ratio */ 

%macro rununi (data,foodvar,modeltype,nloptions,outlib,run); 

/* get nhanes data and rename variables */ 

data biv;

 set &data;

 id = seqn;

 repeat = daycode;

 dayofweek = intake_day;

 recall_food = &foodvar;

 run; 

proc sort data=biv; by id repeat; run; 
 

 

 

 

 

 

 

 

 

 

 

 

 

Page 44  Series 2, No. 178 

/* create covariates for univariate food/nutrient model */ 

data biv;

 set biv;

 if (repeat = 2) then repeat2 = 1;

 else repeat2 = 0; 

run; 

proc sort data=biv; by id repeat; run; 

%NLMixed_Univariate (data = biv,

 lambda = ,

 subject = id,

 repeat = repeat,

 response = recall_food,

 modeltype = &modeltype,

 covars_prob = ,

 covars_amt = repeat2 weekend ,

 replicate_var = psweight_&run,

 nloptions = &nloptions,

 print = Y,

 ntitle = 4);
 

/* save parameter estimates and predicted values. */ 

data outlib.parms_u_&foodvar._&run;

 set parms_u;

 run; 

data outlib.pred_u_&foodvar._&run;

 set pred_x_u;

 run; 

/* delete unneeded data sets 

proc datasets nolist; delete biv b24_boxcox min_a parms_u pred_x_u ; run; quit; 

*/ 

%mend rununi; 
 

 

Series 2, No. 178  Page 45 

************************************************************; 

************************************************************; 

************************************************************; 

/*2: runbivar runs the NCI macro to estimate the bivariate model*/ 

%macro runbivar (data,foodvar1,foodvar2,modeltype,nloptions,outlib,run); 

/* get nhanes data and rename variables */ 

data biv;

 set &data;

 id = seqn;

 repeat = daycode;

 dayofweek = intake_day;

 recall_food1 = &foodvar1;

 recall_food2 = &foodvar2;

 run; 

proc sort data=biv; by id repeat; run; 

/* create covariates for univariate food/nutrient model */ 

data biv;

 set biv;

 if (repeat = 2) then repeat2 = 1;

 else repeat2 = 0; 

run; 

/* get minimum amount on consumption day. */ 

proc means data=biv noprint;

 where (recall_food1 > 0);

 var recall_food1;

 output out=min_a(keep=min_a) min=min_a;

 run; 

data biv;

 merge biv min_a;

 modeltype = upcase(“&modeltype”);

 if (modeltype = “ONEPART” & recall_food1 = 0) then recall_food1 = min_a / 2;

 run; 

data init_parms_f1;

 set outlib.parms_u_&foodvar1._&run; 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Page 46  Series 2, No. 178 

/* rename univariate parameters for bivariate model. */

 rename a_intercept = A1_intercept

 a_repeat2 = A1_repeat2

 a_weekend = A1_weekend

 a_logsde = A1_LogSDe

 a_lambda = A1_Lambda

 logsdu2 = LogSDu2;

 run; 

/* initial estimates for second food/nutrient. */ 

data init_parms_f2;

 set outlib.parms_u_&foodvar2._&run; 

/* rename univariate parameters for bivariate model. */

 rename a_intercept = A2_intercept

 a_repeat2 = A2_repeat2

 a_weekend = A2_weekend

 a_logsde = A2_LogSDe

 a_lambda = A2_Lambda

 logsdu2 = LogSDu3;

 run; 

/* combine initial estimates for the two food/nutrients. */ 

data init_parms;

 merge init_parms_f1 init_parms_f2; 

keep A1_intercept A1_repeat2 A1_weekend A1_Lambda 

A2_intercept A2_repeat2 A2_weekend A2_Lambda 

A1_LogSDe A2_LogSDe 

LogSDu2 LogSDu3;

 run; 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Series 2, No. 178  Page 47 

/* create macro variables for the box-cox transformations for the two food/nutrients. */ 

data lambdas (keep=a1_lambda a2_lambda);

 merge init_parms_f1 init_parms_f2;

 call symput(“lambda1”,trim(left(put(a1_lambda,4.2))));

 call symput(“lambda2”,trim(left(put(a2_lambda,4.2)))); 

run; 

proc print data=lambdas;

 var a1_lambda a2_lambda;

 title3 “Box-Cox Transformation Parameters”;

 run; 

title2; 

%NLMixed_Bivariate (data = biv,

 init_parms = init_parms,

 lambda1 = a1_lambda,

 lambda2 = a2_lambda,

 subject = id,

 repeat = repeat,

 response1 = recall_food1,

 response2 = recall_food2,

 modeltype = &modeltype,

 covars_prob1 = ,

 covars_amt1 = repeat2 weekend ,

 covars_amt2 = repeat2 weekend ,

 replicate_var = psweight_&run,

 nloptions = &nloptions,

 print = Y,

 ntitle = 4);
 

/* save parameter estimates and predicted values. */ 

data outlib.parms_b_&foodvar1._&foodvar2._&run;

 set parms_b;

 run; 

data outlib.pred_b_&foodvar1._&foodvar2._&run;

 set pred_x_b;

 run; 

/* delete unneeded data sets. 

proc datasets lib=work nolist;

 delete biv min_a1 min_a2 init_parms_f1 init_parms_f2 init_parms lambdas;

 run;

 quit; 

*/ 

%mend runbivar; 
 

Page 48  Series 2, No. 178 

************************************************************; 

************************************************************; 

************************************************************; 

/*3: rundist runs the bivariate distrib macro*/ 

%macro rundist(foodvar1, foodvar2, modeltype, seedv, cutpt1, cutpt2, outlib, run);
 

%global seed;
 

%let seed = &seedv; /* seed for random number generator */
 

/* get parameter estimates and predicted values calculated by macro Food_Bivariate. */
 

data parms;

 set outlib.parms_b_&foodvar1._&foodvar2._&run;

 run;
 

data pred;

 set outlib.pred_b_&foodvar1._&foodvar2._&run;

 run;
 

proc sort data=pred; by id repeat; run;
 

/* for predicted values, create two records per subject, one for weekday and one for weekend. */ 

data pred;

 merge pred parms;

 run; 

data pred ;

 set pred;

 by id;

 if (first.id); /* keep first record per subject. */ 

/* the following code assumes that “repeat2” is a covariate in the model: */ 

/* repeat2 = 0 for the first administered 24-hour dietary recall (drddaycd = 1). */ 

/* repeat2 = 1 for the second administered 24-hour dietary recall (drddaycd = 2). */ 

/* for each subject, calculate predicted value when repeat2 = 0. */

 if (repeat2 = 1) then do;

 repeat = 1;

 repeat2 = 0;

 pred_x_a1 = pred_x_a1 - a1_repeat2;

 pred_x_a2 = pred_x_a2 - a2_repeat2;

 end;
 
 

 

Series 2, No. 178  Page 49 

/* the following code assumes that “weekend” is a covariate in the model: */ 

/* weekend = 0 if the 24-hour dietary recall was for Monday-Thursday. */ 

/* weekend = 1 if the 24-hour dietary recall was for Friday-Sunday. */ 

/* create record for weekend = 0. */

 if (weekend = 1) then do;

 weekend = 0;

 pred_x_a1 = pred_x_a1 - a1_weekend;

 pred_x_a2 = pred_x_a2 - a2_weekend;

 end;

 day_wgt = 4/7;

 output;
 

/* create record for weekend = 1. */

 weekend = 1;

 pred_x_a1 = pred_x_a1 + a1_weekend;

 pred_x_a2 = pred_x_a2 + a2_weekend;

 day_wgt = 3/7;

 output;

 run;
 

/* for each food/nutrient, create macro variable that equals half the minimum nonzero amount. */ 

data _null_;

 set pred;

 min_a1 = min_a1 / 2;

 call symput(“min_a1”,trim(left(put(min_a1, best12.))));

 min_a2 = min_a2 / 2;

 call symput(“min_a2”,trim(left(put(min_a2, best12.)))); 

run; 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Page 50  Series 2, No. 178 

/* call macro Distrib_Bivariate to generate monte carlo distribution of usual intake. */ 

%Distrib_Bivariate (param = parms,

 predicted = pred,

 subject = id,

 modeltype = &modeltype,

 nsim_mc = 1000,

 day_wgt = day_wgt,

 min_a1 = &min_a1,

 min_a2 = &min_a2,

 print = N,

 ntitle = 4);
 

options notes; 

title2; 

/* calculate usual nutrient density. */ 

data mcsim;

 set _mcsim; 

/* define nutrient density. */ 

*t_density = 1000 * (t1 / t2); /* nutrient/food per 1000 kcal */ 

*t_density = 100 * ((9 * t1) / t2); /* percent calories from saturated fat */

 t_density=100*(t1/t2); 

run; 

/* call macro Percentiles_Survey to calculate percentiles of usual nutrient density. */ 

/* table 1: by age and gender */ 

title2 “Table 1: Percentiles by Age”; 

data mcsim2;

 set mcsim; 

run; 

%Percentiles_Survey (data = mcsim2,

 byvar = ,

 var = t_density,

 weight = psweight_&run,

 cutpoints = &cutpt1 &cutpt2,

 print = Y,

 ntitle = 4); 

data pctl;

 set _percentiles;

 run; 
 

 

Series 2, No. 178  Page 51 

/* print summary tables */ 

title2 “Estimated Mean and Percentiles”; 

proc print data=pctl label;

 id ;

 var Mean Pctile5 Pctile10 Pctile25 Pctile50 Pctile75 Pctile90 Pctile95;

 format Mean Pctile5 Pctile10 Pctile25 Pctile50 Pctile75 Pctile90 Pctile95 7.2;

 run; 

/* save means, percentiles and cut-point probabilities. */ 

data outlib.pctl_b_&foodvar1._&foodvar2._&run;

 set pctl;

 run=&run;

 run; 

/* delete unneeded data sets. */ 

proc datasets nolist;

 delete parms pred mcsim _mcsim mcsim2 pctl _percentiles;

 run;

 quit; 

%mend rundist; 

************************************************************; 

************************************************************; 

************************************************************; 

/*4: bivrep repeats the univariate and bivariate runs for each replicate*/ 

%macro bivrep(data, run,outlib,foodvar1,foodvar2,cutpt1,cutpt2);
 

%do run=1 %to &run;
 

options nonotes;
 

%put BRR Run &run &foodvar1;
 

%rununi (data=&data,foodvar=&foodvar1,modeltype=ONEPART,nloptions=technique=trureg maxfunc=10000 maxiter=1000,outlib=&outlib,
 

run=&run);
 

%rununi (data=&data,foodvar=&foodvar2,modeltype=ONEPART,nloptions=technique=trureg maxfunc=10000 maxiter=1000,outlib=&outlib,
 

run=&run);
 
Page 52  Series 2, No. 178 

%runbivar (data=&data,foodvar1=&foodvar1, foodvar2=&foodvar2, modeltype=ONEPART, nloptions=technique=trureg maxfunc=10000 maxiter=1000,
 

outlib=&outlib,run=&run);
 

%rundist(foodvar1=&foodvar1, foodvar2=&foodvar2, modeltype=ONEPART, seedv=12345, outlib=&outlib,run=&run, cutpt1=&cutpt1, cutpt2=&cutpt2);
 

proc append base=brr_runs data=outlib.pctl_b_&foodvar1._&foodvar2._&run;
 

%end;
 

data outlib.brr_&foodvar1._&run; 

set brr_runs;
 

rename mean=bmean pctile1-pctile99=bpctile1-bpctile99;
 

run;
 

%mend bivrep;
 

************************************************************;
 

************************************************************;
 

************************************************************;
 

/*5: means_set appends the output from each replicate and uses the 

brr_pvalue_ci macro to estimate SEs for the bivariate distribution*/ 

%macro means_set(foodvar1, foodvar2,replicfirst,repliclast, degfree,subgroup); 

%put &foodvar1 ; 

data outlib.brr_biv_&foodvar1; 

set outlib.pctl_b_&foodvar1._&foodvar2._&replicfirst-outlib.pctl_b_&foodvar1._&foodvar2._&repliclast; 

run; 

data origwt_paramest_&foodvar1 brr_paramest_&foodvar1;

 set outlib.brr_biv_&foodvar1;

 if run=0 then output origwt_paramest_&foodvar1;

 else output brr_paramest_&foodvar1; 

run; 
 

 

 

 

 

 

 

 

 

Series 2, No. 178  Page 53 

*************************; 

*************************; 

* BRR Calcs *; 

*************************; 

*************************; 

%brr_pvalue_ci(data_orig_estimates_1rec = origwt_paramest_&foodvar1, 

data_brr_estimates_brecs = brr_paramest_&foodvar1,

 param_estimate_names = mean pctile5 pctile25 pctile50 pctile75 pctile95, 

set_f_method_fay = 0.3, 

set_confidence_level = 0.95, 

set_degrees_freedom = °free, 

print = y, 

titles = 4

 ); 

%mend; 

************************************************************; 

************************************************************; 

************************************************************; 

/*process data to generate ‘datafile’ here*/ 

; 

/*run 2 univariate, bivariate, and bivariate distrib */ 

%rununi (data=datafile,foodvar=tfatkcal,modeltype=ONEPART,nloptions=technique=trureg maxfunc=10000
 

maxiter=1000,outlib=outlib,
 

run=0);
 

%rununi (data=datafile,foodvar=energy,modeltype=ONEPART,nloptions=technique=trureg maxfunc=10000
 

maxiter=1000,outlib=outlib,
 

run=0);
 

%runbivar (data=datafile,foodvar1=tfatkcal, foodvar2=energy, modeltype=ONEPART, nloptions=technique=trureg maxfunc=10000 maxiter=1000,
 

outlib=outlib,run=0);
 

%rundist (foodvar1=tfatkcal, foodvar2=energy, modeltype=ONEPART, seedv=12345, outlib=outlib,run=0); 

*----------------------------------------------------------------------------------------------------------; 

*----------- NOW, REPEAT THESE STEPS FOR THE 32 BRR RUNS AND GET SEs 

------------; 

*----------------------------------------------------------------------------------------------------------; 

%bivrep(data=datafile, run=32, foodvar1=tfatkcal, foodvar2=energy, outlib=outlib, cutpt1=, cutpt2=); 

%means_set(foodvar1=tfatkcal, foodvar2=energy,replicfirst=0,repliclast=32, degfree=31); 
Page 54  Series 2, No. 178 

Appendix VII. Empirical Approach to Selecting a Correlated or 

Uncorrelated Model 

Run both models and then test the 

parameter corresponding to the Fisher’s 

Z-transformation of the correlation 

between random effects in the probability 

to consume and amount consumed parts 

of the for statistical significance using a 

t test where the standard error is 

computed via balanced repeated 

replication (BRR) (holding the lambda 

constant across BRR runs). If many 

dietary components are to be assessed 

this way, using the most parsimonious 

model for each component, the set of 

tests should be adjusted for multiple 

comparisons (e.g., using the methods 

described in a report by Benjamini & 

Hochberg, (1995) [64]). Briefly, this 

method uses a false discovery rate 

procedure to test the strength of the 

correlation between the two parts of 

the model (54) and allows analysts to 

proceed with the correlated model in 

cases where there is strong evidence of a 

significant correlation. If there is truly no 

correlation, using the correlated model 

will generally lead to more variable 

estimates. On the other hand, the simpler 

uncorrelated model will generally 

have less variability but more bias if 

the correlation is truly present (54). 

Implementing the Benjamini & Hochberg 

adjustment involves ranking the K p 

values for the K individual tests from 

smallest to largest, selecting a value for 

the desired false Discovery Rate (e.g., 

p = .10), and then for m = 1, ..., K, reject 

null hypothesis if m-th smallest p value is 

less than (.10*m)/K. 
Vital and Health Statistics 

Series Descriptions 

Active Series 

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Reports describe the programs and data systems of the 

National Center for Health Statistics, and the data collection 

and survey methods used. Series 1 reports also include 

definitions, survey design, estimation, and other material 

necessary for understanding and analyzing the data. 

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Reports present new statistical methodology including 

experimental tests of new survey methods, studies of vital and 

health statistics collection methods, new analytical techniques, 

objective evaluations of reliability of collected data, and 

contributions to statistical theory. Reports also include 

comparison of U.S. methodology with those of other countries. 

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Reports present data analyses, epidemiological studies, and 

descriptive statistics based on national surveys and data 

systems. As of 2015, Series 3 includes reports that would 

have previously been published in Series 5, 10–15, and 20–23. 

Discontinued Series 

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Reports contain findings of major committees concerned with 

vital and health statistics and documents. The last Series 4 

report was published in 2002; these are now included in 

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The last Series 5 report was published in 2003; these are now 

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Reports use methods of cognitive science to design, evaluate, 

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Reports present 1) estimates of the medically defined 

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physiological, and psychological characteristics and 2) 

analysis of relationships among the various measurements. 

As of 2015, these are included in Series 3. 

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The last Series 12 report was published in 1974; these reports 

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Reports present statistics on health resources and use of 

health care resources based on data collected from health 

care providers and provider records. As of 2015, these reports 

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The last Series 14 report was published in 1989; these reports 

were included in Series 13, and are now included in Series 3. 

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Reports contain statistics on health and health-related topics 

from surveys that are not a part of the continuing data systems 

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report was published in 2002; these reports are now included 

in Series 3. 

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The last Series 16 report was published in 1996. All reports 

are available online; compilations are no longer needed. 

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Reports include analyses by cause of death and demographic 

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Divorce 

The last Series 24 report was published in 1996. All reports 

are available online; compilations are no longer needed. 

For answers to questions about this report or for a list of reports published 

in these series, contact: 

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3311 Toledo Road, Room 4551, MS P08 

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