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Fillable Printable Sample Template For Project Proposal

Fillable Printable Sample Template For Project Proposal

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
1
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
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
Email
Phone
555-555-5555
555-555-1234
Institution
University
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
Y
Y
2
Complete as applicable for your project. Address any “Yes” responses in the body of the proposal.
YES
NO
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
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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 618 years examined in the National Health and Nutrition Examination Survey (NHANES)
from 20032006, and evaluated the relationship between serum vitamin D levels and obesity, defined as
a body mass index (BMI) 95
th
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
th
percentile for age, in children aged 618 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-
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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 20032006. The outcome is serum vitamin D level and is
dichotomized into deficient” (<15ng/ml or the 10
th
percentile) or not deficient ( 15ng/ml).
The primary explanatory variable is obesity (or BMI 95
th
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 69 years, 1012 years, 1315 years, and 1618 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).
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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 618 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
th
,
10
th
, and 25
th
percentile cutpoints. We may present individual histograms by age group as well
(e.g., 612 year olds and 1318 year olds). It will be created from output that looks like this:
BMI Percentile
5
10
25
50
75
85
90
95
100
Vitamin D
>=15ng/ml
Vitamin D
< 15ng/ml
10
Table 1: Summary statistics of the weighted percentage breakdown for each covariate and the
outcome
(vitamin D deficiency) in the sample population of 618 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
6
7
8
9
10
11
12
13
14
15
16
17
18
Sex
Male
Female
Race
White
African
American
Mexican
American
Other
Ethnicity
Hispanic
Non-Hispanic
Vitamin D
Supplement Use
Yes
No
Season
Winter
Spring
Summer
Fall
Latitude
North
South
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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
th
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=20032004, 4=20052006)
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
th
percentile), or no (serum 25-OH vitamin D 15ng/ml or ≥ 10
th
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
th
percentile) or no (BMIPCT
<95
th
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 69 years, 1012 years, 1315
years, and 1618 years
SEX
= RIAGENDR = Subject’s sex
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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
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