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NHANES 1999-2004 Analytic Strategies

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Centers for Disease Control and Prevention

National Center for Health Statistics

NHANES 1999-2004

Analytic Strategies

Deanna Kruszon-Moran, MS

Downloadingdemographic, questionnaire, exam and lab files.

- Files are no longer available as self-extracting zip files.
- Documentation and procedure files are now in Adobe PDF format and can be viewed or accessed directly via the web link
- Clicking on the data link will allow you to store the data file or open it directly with SAS.
- Data files are in SAS transport (.xpt) format.

- Read the documentation !!
- Read the documentation !!
- Read the documentation !!
- Read the documentation!!

Merging:

- Merge all files by sequence number to the demographic file.
- Verify the numbers of records merged and the final sample number against the published frequencies on the web.
- Be sure they are what you expected and all merges worked correctly.

- Run basic frequencies and cross tabulations.
- Know your target population.
- Understand how item was measured
- (how is the item defined, topcoded, recoded)

- Recode variables as necessary
- (example: age groups, positive/negative lab tests, high/low BP, high/low cholesterol etc.).

- Recode unknown/refusals as missing data
(77, 99 recode to missing).

- Check your coding – run frequencies in SAS.

Continuous Outcome Data:

- Look for outliers in your measure.
- Run Proc Univariate.

- Look for outliers among the weights.
- Use Proc Univariate on the weight variable.
- Outlying variables especially those with large weights can really influence your estimates.

- Look at normality.
- Consider transformations.
- Log, square root, power.

NHANES is a complex, multistage,

probability cluster design of the civilian,

noninstitutionalized US population.

To analyze NHANES data you must use the sample weights to account for :

Stage 1

Counties

Stage 2

Segments

Stage 3

Households

Stage 4

Individuals

1. The base probability of selection

NHANE 1999-2004 - Oversampled

- African Americans
- Mexican Americans
- Persons with low income
- Adolescents aged 12-19
- Persons aged 60+

Screening interview

N=13312

Interview

Non-response

22%

Household interview

N=10291

78%

Exam

Non-response

7%

MEC Exam

N=9471

71%

Non-response:

- Most components have some level of individual item or component non-response.
- ONLY non-response to the interview and exam has already been accounted for in the weights.
- All additional non-response to the outcome measure of interest should be examined against all possible predictors.
- Potential biases should be discussed.
- If non-response is “high”, re-weighting should be considered.

For 4 years of data from 2001-2004 -

MEC4YR = 1/2 WTMEC2YR ;

For 6 years of data from 1999-2004 –

if sddsrvyr=1 or sddsrvyr=2 then

MEC6YR = 2/3 WTMEC4YR ; /* for 1999-2002 */

If sddsrvyr=3 then

MEC6YR = 1/3 WTMEC2YR ; /* for 2003-2004 */

* Only when analyzing years 1999-2002, you should not combined 2 year weights but use the 4 year weights provided.

Future years of data will be combined similarly:

For 6 years of data from 2001-2006 -

if sddsrvyr in (2,3,4) then

MEC6YR = 1/3 WTMEC2YR;

For 8 years of data from 1999-2006 –

if sddsrvyr=1 or sddsrvyr=2 then

MEC8YR = 1/2 WTMEC4YR ; /* for 1999-2002 */

if sddsrvyr=3 or sddsrvyr=4 then

MEC8YR = 1/4 WTMEC2YR etc; /* for 2003-2006 */

Subsamples and appropriate weights:

- Look at your primary variable of interest and the corresponding weight.
- Look at all other variables you want to combine with it.
- Are all from the interview? Exam? Subsample (i.e. fasting, audiometry, dioxin, VOC’s …) ?
- Use the weight from the smallest subsample for your analysis.
- Be consistent!

Subsamples and appropriate weights:

- Be careful about combining subsamples beyond MEC + VOC’s, Interview + Dioxin etc.
- Combining subsamples such as Environmental + AM fasting could be problematic.
- Some subsamples are mutually exclusive.
- Weights were not designed for combining subsamples and may not produce good estimates.

Subsetting the data for SUDAAN:

- If using MEC exam weights - SUBSET the data on those MEC EXAMINED in SAS before using SUDAAN.
- If using other subsample weights – subset the data on those in the subsample corresponding to the weights you are using.
- Then use the SUBPOPN statement in the SUDAAN procedure to further subset your data by age, gender etc. to reflect the target population you are interested in analyzing.

Example:

You are interested in examining the association of high triglycerides, blood pressure, and body mass index (BMI) controlling for race/ethnicity on females age 20-59 from the 6 years of data from 1999-2004.

Step 1 – Determine the smallestsample population for the analysis to determine the correct weight to use.

- Race/ethnicity, gender and age are in the interview.
- Blood pressure and weight come from the MEC exam a subset of those interviewed.
- Triglycerides were measured on a subsample of those MEC examined who fasted for 8 hours and came to the AM MEC exam.
- Therefore, the fasting subsample is the smallest subsample in the analysis and you would use the AM fasting weights (WTSAF2YR and WTSAF4YR).

Step 2 – Combine weights in SAS prior to the SUDAAN procedure for the 6 years from 1999-2004:

If sddsrvyr in (1,2) then

WEIGHT6 =2/3*WTSAF4YR ; /* 1999-2002 */

If sddsrvyr=3 then

WEIGHT6= 1/3*WTSAF2YR ; /* 2003-2004*/

Step 3 – Subset your data set in SAS to reflect the weight being used (AM fasting weights WTSAF2YR or WTSAF4YR):

SAS Code:

IF WTSAF2YR ne . or WTSAF4YR ne . ;

Step4 – Last specify the correct weight to use using the weight statement in SUDAAN

and subset your data to obtain the subpopulation of interest using the SUBPOPN statement in SUDAAN (females age 20-59):

WEIGHT WEIGHT6 ;

SUBPOPN riagendr=2 and ridageyr > 19 and ridageyr < 60 ;

Why must you use the sample design to estimate the variance?

- NHANES is a cluster design
- Individual within a cluster are more similar than those in other clusters.
- This homogeneity or clustering results in a reduction of our effective sample size because we choose individuals within cluster vs randomly throughout the population.

Why must you use the sample design to estimate the variance?

- Variance estimates that do not account for this intra cluster correlation are too low and biased.
- Survey software such as SUDAAN or SAS Survey procedures must be used to account for the complex design and produce unbiased variance estimates
- These procedures require information on the sample design (i.e. identification of the PSU and strata) for each sample person.

For the initial 1999-2000 data release we recommended:

- Using JK-1/Jackknife/”leave-one-out” procedure.
- Required 52 replicate weights for each of 52 groups created. Only provided for 1999-2000.
- Can still be used if you have software that can produce the replicate weights.
- Replicate weights for this procedure will no longer be created on the data set.
- Too cumbersome

We now recommend:

Using the Taylor series (linearization) method

- Same as that used in NHANES III.
- We now provide “Masked Variance Units” (MVU’s) in place of primary sampling units (PSU’s) to maintain confidentiality.
- Design variables are called - SDMVSTRA and SDMVPSU.

SDMVSTRA and SDMVPSU

- Found in the demographic file.
- Found in all two year data sets and can be combined for 4 or 6 or … year data sets.
- Can be used the same as the actual stratum and PSU variables.
- Produce variance estimates close to those using the “true” design.
- Data MUST be sorted by SDMVSTRA and SDMVPSU first, before using SUDAAN.

In SAS:

IF WTMEC2YR NE . ; (Include only those with weights)

PROC SORT OUT=Datasort ;

BY SDMVSTRA SDMVPSU; (sort on design variables)

SUDAAN code :

PROC Descript DATA=Datasort DESIGN=WR ;

NEST SDMVSTRA SDMVPSU ;

WEIGHT WTMEC2YR ;

SUBPOPN RIDAGEYR > 11 AND RIDAGEYR < 50 AND TOXTEST=1 ;

TABLES Raceth2*Sex ;

SAS code (Do not use WHERE or BY in procedure) :

If RIDAGEYR > 11 AND RIDAGEYR < 50 AND TOXTEST=1 then INCLSP=1 ; else INCLSP=2 ;

PROC Surveymeans data=data ;

Strata SDMVSTRA;

Cluster SDMVPSU;

Weight WTMEC2YR ;

Domain INCLSP INCLSP*RACETH2 INCLSP*SEX INCLSP*RACETH*SEX;

Calculating Population Totals

- Estimates of the number of persons in the U.S. population with a particular condition must be done carefully.
- Recommended procedure is to:
- First, estimate the proportion with the condition for each subdomain of interest.
- Mutliply that by the population control totals for that subdomain.

- Tables are available on the NCHS web site with the current March 2001 CPS control totals as part of the analytic guidelines.

Calculating Population Totals

- Estimates of number of persons with a condition can be obtained by summing the weights of those positive.
- These estimates will be less reliable due to
- item non response
- and sampling error

- Not the recommended method.

Things to consider:

- Data released in two year cycles.
- We STRONGLY RECOMMEND using two or more cycles (4 or more years )to produce reliable estimates.
- Verify data items collected were comparable in wording and methods.
- When combining years remember to use correct combined weights.

Things to consider:

- What is your sample from each survey–age?
- How different was the question worded or the interview methods ?
- How different were the lab or exam methodologies ? Cutoffs used? Definitions?
- For current NHANES 1999-2004 sample sizes may be smaller depending on number of years measured - especially in sub domains
- Larger sampling variation.
- May need to limit comparisons.

Two variables available

RIDRETH1

&

RIDRETH2

Ridreth1- Use for analyses of 1999-2004 data alone.

1=Mexican American

2=other Hispanic

3=non-Hispanic white

4=non-Hispanic black

5=other races including multiracial.

- For 2 and 4 years of data we know there is insufficient sample size to analyze “other Hispanics” (group 2) alone or to analyze “all Hispanics”.
- Analyses to evaluate whether 6 years of data (1999-2004) are sufficient to analyze these Hispanic groups are ongoing.
- Groups 2 and 5 can AND should continue to be combined to represent all other races.

Ridreth2

Use for analyzing trends from NHANES III to NHANES 1999-2004.

Most comparable to race/ethnicity variable collected in NHANES III.

Coded as :

1=non-Hispanic white

2=non-Hispanic black

3=Mexican American

4=other – including Multi-Racial

5=other Hispanic

Crude versus Age Standardized Estimates:

- Age distributions within survey samples vary by racial/ethnic group.
- Age distributions also vary by survey – NHANES III vs. NHANES 1999-2004.
- When comparing estimates across racial/ethnic groups or between surveys you may need to age standardize.
- Also present all age specific estimates!

When Age Standardizing:

- Use the 2000 U.S. Census Population for consistency for both NHANES III and all NHANES 1999-2000 or above.
- For guidelines and population proportions see the website below for the Klein and Schoenborn HP2010 Statistical Notes on “Age Adjustment using the 2000 Projected U.S. Population”.
http://www.cdc.gov/nchs/data/statnt/statnt20.pdf

When Age Standardizing:

- In SUDAAN, use the STDVAR and STDWGT statements.
- STDVAR –variable name for the age groups.
- STDWGT – corresponding proportion of the 2000 U.S. Census population for that age subgroup.

Crude vs. Age Standardized Estimates Example:

Design Effect

- Sample design effect - the ratio of the variance estimated under the complex sample design to the variance under simple random sampling
Var (CSD) / Var (SRS)

- SUDAAN - DEFT2 option in Proc Descript
- Design effect can be averaged

Effective Sample sizes

- Sample sizes should be adjusted by the sample design effect (DEFF)
Effective N = N/DEFF

- Minimum sample size for reporting each individual estimate depends on the statistic being calculated, its relative size, stability of the SE estimate, degrees of freedom and other special circumstances.
- Please refer to the Analytic Guidelines on our web site for more details.

Relative Standard Errors :

- For estimates such as means/prevalences – calculate the relative standard error (RSE) as follows: (SE mean / mean) X 100%
- For prevalence estimates near 100% (i.e. > 90%), look at the RSE for the percent negative not just percent positive.
- i.e. calculate RSE for minimum p or 1-p

Relative Standard Errors and “Rare” Events:

- RSE’s <20%, estimates are most likely reportable.
- RSE’s >30%, consider whether the estimate provides useful information.
- Estimates of 50% with SE of 15% and RSE of 30% give a 95% CI’s approximately 20-80%. Is this really useful information?
- Estimates of low prevalence (i.e. 5%) with SE of 1.5 also give RSE of 30% but the 95% CI is approximately 2-8%. This may be very useful information.

Confidence Limits for“rare” (>90% or <10%) events:

- Standard normal approaches for calculating 95% CI’s may give lower bounds < 0 or upper bounds > 100.
- Statistical literature describes alternative methods under these situations.
- Evaluation of these various methods - see analytic guidelines on NCHS web site.

Degrees of freedom (DF) for t-statistics:

- Must calculate the DF to obtain a correct t-statistic for calculating confidence limits.
- DF are = number of clusters in the 2nd level of sampling (# PSU’s) – number of clusters in the 1st level of sampling (#strata) in your subgroup of interest.
- Same for both SAS and SUDAAN when all strata and PSU’s are represented in your subgroup.

Degrees of freedom (DF) for t-statistics:

- SAS and SUDAAN do not calculate DF the same way when your subgroup is NOT represented in all PSU’s and strata.
- SAS is currently working on correcting this.
- In SUDAAN, to calculate DF you must output the # strata and the # PSU’s using the ATLEVL1=1 and ATLEV2=2 options in your PROC Descript or PROC Crosstab

Analytic Guidelines:

- Detailed guidelines for working with NHANES data can be found at:
http://www.cdc.gov/nchs/nhanes.htm

- This document contains everything discussed today and will continue to grow to include guidelines for statistical tests, multivariate analyses, modeling and more!
- Web based tutorial also currently available and continuously being developed.