Fillable Printable Sample Template For Project Proposal

Sample Template For Project Proposal

Sample Proposal (Revised 9/12/2012)

The Association Between Serum Vitamin D Levels and Childhood Obesity, Ima Researcher

RDC Research Proposal

General Information

Date:

February 25, 2009

Title of Project:

The Association Between Serum Vitamin D Levels and Childhood

Obesity

NCHS Data System and Years:

NHANES 2003-2006

Non-NCHS Data Files:

N/A

Mode of Access:

(Check all that apply)

[X] NCHS RDC, Hyattsville, MD (Washington, DC-metro)

[_] NCHS RDC, Atlanta, GA

[_] Remote Access (ANDRE)

[_] Census RDC, specify: ___________________

Statistical Software:

(Check all that apply)

[X] SAS/Sudaan [_] Stata [_] Other, specify: ________________

* Remote access users can only use SAS/Sudaan

Proposed Start Date:

May 1, 2009

Funding Source:

Funded by the National Insitute for Obesity Research, Grant No. 555

Billing Address:

(include contact person)

Ima Business Manager

University

Department

1234 Research Way, Room 789

City, State, 12345

[email protected]om

555-555-7890

Complete as applicable for your project. Everyone listed in this section will need to submit a C.V. and if

approved, must complete the Confidentiality Orientation. There can only be one ANDRE programmer.

Research Team

Primary Investigator

Programmer

[X] On-site or [_] ANDRE (account holder)

Name

Ima Researcher

Ima Programmer

imaresearcher@email.com

imaprogrammer@email.com

Phone

555-555-5555

555-555-1234

Institution

University

Mailing Address

Department

1234 Research Way, Room 789

City, State, 12345

Department

1234 Research Way, Room 789

City, State, 12345

US Citizen? Y or N

Complete as applicable for your project. Address any “Yes” responses in the body of the proposal.

RDC Proposal Summary Information

YES

Geographic variables

Level of geography to be shown in output (check all that apply)

National

Regional

State

MSA

County

Urban/rural classification

Census tract

Latitude/Longitude

Other

Will geographic identifier(s) be removed after merge

If yes, can true geographic identifiers be replaced with masked versions of these variables

Is GIS or mapping proposed

Dates and Temporal information

Are exact dates requested other than to calculate time of follow-up

If more than 1 year/cycle, will years/cycles be presented separately

Merging of data with NCHS restricted data

Are external data being merged with NCHS data

Linked Data Products

Are you requesting linked Medicare/Medicaid files

If yes, are you using multiple years

Are you using public-use mortality data

Research Proposal

A. Abstract:

Obesity has been linked to vitamin D deficiency in adults and adolescents. We aimed to

determine if an association exists between obesity and inadequate serum vitamin D levels among U.S.

children.

We used serum 25-hydroxyvitamin D (vitamin D) and body measurement data from 4,745 U.S.

children aged 6–18 years examined in the National Health and Nutrition Examination Survey (NHANES)

from 2003–2006, and evaluated the relationship between serum vitamin D levels and obesity, defined as

a body mass index (BMI) ≥ 95

percentile. Vitamin D levels were dichotomized as deficient (<15ng/ml)

or not deficient in logistic regression models to assess odds of vitamin D deficiency accounting for age,

sex, race/ethnicity, poverty status, and vitamin D-containing supplement use. We seek to adjust for two

additional factors associated with serum vitamin D levels that may influence our results: latitude of

residence and month/season of lab testing. These variables are restricted and only available through the

Research Data Center.

B. Research Question:

 What is the relationship between vitamin D deficiency and obesity in US children aged 6-18

years?

 How does latitude of residence and month/season of lab testing influence this relationship?

C. Background:

Vitamin D is a fat-soluble vitamin needed for promoting calcium absorption in the gut and

ultimately enabling normal bone mineralization. It is also needed for bone growth and remodeling and

has more recently been discovered to be involved in other physiologic processes, including modulation

of neuromuscular and immune function, as well as reduction of inflammation. It may also play a role in

modulating cancer cell proliferation. The growing evidence that vitamin D may help prevent several

chronic diseases prompts the need to identify individuals at risk for vitamin D deficiency.

Humans get vitamin D from their diet, in dietary supplements, and from exposure to sunlight.

People living at higher latitudes have been shown to have lower levels of serum vitamin D compared

with those living in lower latitudes, and levels of serum vitamin D are highest during the summer

months when sun exposure is greater.

Examination of the relationship between serum vitamin D levels and obesity is done using

logistic regression analysis, with vitamin D deficiency as the binomial outcome and obesity as the

binomial primary explanatory variable. Other important covariates we have adjusted for in our analyses

thus far (using publicly accessible NHANES data) include: age, sex, race/ethnicity, poverty status, and the

use of vitamin D-containing supplements. Because exposure to sunlight is also an important factor to

account for when measuring serum vitamin D levels, we want to adjust for latitude of residence and

month/season of lab draw, in addition to the other mentioned covariates. These variables are only

available through the RDC.

D. Public Health Benefit

Our study seeks to examine the relationship between serum vitamin D levels, measured as 25-

hydroxyvitamin D and dichotomized as vitamin D deficient or not, and obesity, defined as a BMI ≥ 95

percentile for age, in children aged 6–18 years. Prior research in adolescents and adults has shown a

positive association between vitamin D deficiency and obesity. By establishing an association between

low serum vitamin D levels and obesity in children across a wider age range, we aim to identify an easy-

to-obtain and objective measure with which to target children who may be at greater risk for vitamin D

deficiency. Using this measure, children deficient in vitamin D may be more readily identified and started

on supplementation to correct it. Because vitamin D may be involved in improving other health measures

or preventing other chronic diseases or conditions, treating deficiency may have benefits that extend

beyond improved bone health.

E. Data Requirements:

1. NCHS Survey, Years, Files

NHANES

2003-2006

Demographic variables and sample weights

Physical examination measurements

Lab component: Vitamin D

Dietary supplements questionnaire

Dietary supplements questionnaire formats

2. Restricted Variables

LAT = Location (latitude) of residence will be used to control for sun exposure.

Month of MEC exam/lab draw will be used to control for season.

3. Non-NCHS Data

N/A

4. Merge Variables

i) SEQN will be used to merge the public and restricted data files

ii) N/A

F. Methodology:

1. Unit or Level of Analysis and Subpopulation(s):

Unit of Analysis – individual

Subpopulation – Children 6-18

2. Analysis Plan:

We have already performed logistic regression analyses using SAS-callable SUDAAN on the

public use NHANES data for 2003–2006. The outcome is serum vitamin D level and is

dichotomized into “deficient” (<15ng/ml or the 10

percentile) or “not deficient” (≥ 15ng/ml).

The primary explanatory variable is obesity (or BMI ≥ 95

percentile for age) and is categorized

as yes or no. The remaining covariates include: age (as a continuous variable, in years, for one

analysis), age group (categorized into 6–9 years, 10–12 years, 13–15 years, and 16–18 years, for

a separate analysis), sex (male or female), race/ethnicity (categorized as Non-Hispanic white,

Non-Hispanic black, Mexican American, and other races, including multiracial), poverty status

(categorized as <2.0 PIR or ≥ 2.0 PIR), and vitamin D-containing supplement use (yes or no).

We intend on adjusting for two additional covariates obtained from the RDC: latitude of

residence (specific latitude broken down into ranges, range size dependent on the variability of

latitudes); season of MEC lab draw (determined by the month/date of the MEC exam/lab draw).

3. Complex Survey Design:

Our codes already account for weighting and a complex sample design, where WTMEC4YR = 1/2

x WTMEC2YR for this 4-year sample.

Example logistic regression code follows:

proc rlogist data = out.vitdobese_4 filetype=sas design=wr;

nest sdmvstra sdmvpsu/missunit;

weight wtmec4yr;

subpopn include = 1/name="6-18 year olds, no pregnant females";

class obese sex raceth fampir vitdsup/nofreqs;

reflevel obese=0 sex=1 raceth=1 fampir=2 vitdsup=2;

model vitd10 = obese sex raceth fampir vitdsup examageyr;

etc.

G. Output:

1. Overview:

We will present a weighted histogram of serum vitamin D levels for the sample population of

all

children 6–18 years of age, excluding pregnant females, those with implausible BMIs, and

those with missing covariate data. This will help establish our choice of cutpoint used to

define vitamin D deficiency. Next, we will present proportions/percentages of the population

for each covariate (age, in years, and age group, sex, race/ethnicity, latitude of residence

(range), season of exam/lab draw, poverty status, vitamin D supplement use, obesity) and the

outcome of vitamin D deficiency. We will then perform univariate analyses and present a

proportional breakdown of each covariate with vitamin D deficiency. And finally, we will

perform logistic regression analyses to determine odds ratios for vitamin D deficiency,

adjusting for all of the above-mentioned covariates. Our primary focus will be on the odds

ratio for vitamin D deficiency in obese vs. non-obese children, but we may present other

significant odds ratios as well. We will perform one analysis with age as a continuous variable

and another with age group as a categorical variable. If any significant interactions are found,

we will also present those.

2. Examples/Table Shells: The desired output will consist of the following graphs/tables

Figure 1: Weighted histogram of serum vitamin D levels in the sample population, with labeled 5

, and 25

percentile cutpoints. We may present individual histograms by age group as well

(e.g., 6–12 year olds and 13–18 year olds). It will be created from output that looks like this:

BMI Percentile

100

Vitamin D

>=15ng/ml

Vitamin D

< 15ng/ml

Table 1: Summary statistics of the weighted percentage breakdown for each covariate and the

outcome

(vitamin D deficiency) in the sample population of 6–18 year olds.

Table 2: Results of any significant univariate analyses for vitamin D deficiency status and each

covariate, presented in odds ratios.

Table 1: %

Table 2: OR

Vitamin D

>= 15 ng/ml

Vitamin D

< 15 ng/ml

Age

Sex

Male

Female

Race

White

African

American

Mexican

American

Other

Ethnicity

Hispanic

Non-Hispanic

Vitamin D

Supplement Use

Yes

Season

Winter

Spring

Summer

Fall

Latitude

North

South

Table 3: Results of logistic regression analyses for vitamin D deficiency, adjusted for age (in years, as a

continuous variable) or age group (as a categorical variable), sex, race/ethnicity, poverty status,

latitude of residence, season of exam, vitamin D supplement use presented as adjusted odds ratios. If

any significant interactions are found, they will also be presented, with corresponding p-values.

Vitamin D

>= 15 ng/ml

Vitamin D

< 15 ng/ml

BMI >95

Percentile

* controlling for age, sex, race/ethnicity, poverty,

latitude, season, supplement use

3. Presentation of Results: Presentation to EIS officers and potential peer-review publication.

H. Data Dictionary:

1. NCHS Restricted Data Dictionary

Variable Name

Variable Description

SEQN

Sequence Number – Used for Merging to Public Data

LAT

Latitude of residence

EXAMDATE

Date of MEC exam/lab draw

2. NCHS Public Use Data Dictionary

Variable Name

Variable Description

SEQN

Sequence Number – Used for Merging to Public Data

SDMVSTRA

Pseudo-stratum, used to identify segment in individual counties

SDMVPSU

Pseudo-primary sampling unit, used to identify households

SDDSRVYR

Survey year (3=2003–2004, 4=2005–2006)

WTMEC4YR

½ x WTMEC2YR, used to extrapolate sample data to entire population for the

entire 4-year study period

RIDEXPRG

Pregnancy status of participant

VIT_D = LBXVID

Serum 25-OH vitamin D level, in ng/ml

VITD10

Vitamin D deficiency: yes (serum 25-OH vitamin D <15ng/ml or <10

percentile), or no (serum 25-OH vitamin D ≥ 15ng/ml or ≥ 10

percentile)

BMIPCT

BMI percentile for age (in months), calculated with a pre-written program

using height (BMXHT) and weight (BMXWT) variables measured on bmx data

sets

OBESE

Obesity status, categorized as yes (BMIPCT ≥ 95

percentile) or no (BMIPCT

<95

percentile)

EXAMAGEYR

Age, in years (converted from RIDAGEEX or age, in months, at time of MEC

exam, divided by 12)

AGEGROUP

Age, in years (EXAMAGEYR), categorized as 6–9 years, 10–12 years, 13–15

years, and 16–18 years

SEX

= RIAGENDR = Subject’s sex

RACETH

Race/ethnicity (same as RIDRETH1, except for adding “Other Hispanic” into

the “Other race, including multiracial” category)

FAMPIR

Poverty status (INDFMPIR categorized as a poverty income ratio <2.0 or ≥ 2.0)

VITDSUP

Vitamin D-containing supplement use (determined by finding any dietary

supplements taken by each participant that contained an ingredient of

vitamin D), categorized as yes or no

INCLUDE

Sample selection variable, which includes only

3. Non-NCHS Data Dictionary: N/A

I. References:

Please limit to 10

J. Other Authors:

Other Author One, University

Other Author Two, University

K. Resumes/CV:

Please limit each CV to 2 pages