SUDAAN is commonly used to analyze data from such NCHS studies as the National Health Interview Survey (NHIS), National Health and Nutrition Examination Survey (NHANES), National Survey of Family Growth (NSFG) and many more. The NCHS web site contains many examples. Below are some links to useful information:
Sample Design, Sampling Weights, Imputation, and Variance Estimation in the 1995 National Survey of Family Growth (NSFG). This is a PDF document that includes several example programs using SUDAAN to analyze NSFG data. (Use Adobe Acrobat Reader to view.)
You can also search for further information and examples about using SUDAAN with NCHS and CDC data by using either the NCHS Web Search or the CDC Web Search sites. Enter SUDAAN as the search word and click SEARCH.
Click here to see our collection of SUDAAN examples. Each example is packaged as a downloadable self-extracting executable. Each executable contains both standalone and SAS-callable versions of the program, data and output for the procedure
SUDAAN uses a robust variance estimator that properly accounts implicitly for any number of stages of nesting. This approach is used in all of the SUDAAN procedures and all variance estimates. Therefore, hypothesis tests and confidence intervals also account for all stages of nesting. The critical assumption is that the primary, or first-stage, clusters are independent of each other. If this is true, then any number of nesting stages can occur within the primary clusters and SUDAAN will yield valid inferences.
In the above situation, you use the DESIGN=WR option on the PROC statement. With this option, you only need to identify the primary clusters. On the NEST statement, list any strata or blocks used to collect the data, followed by the primary clusters. For example, consider a dental experiment where measurements are taken on each tooth surface from all teeth from a set of patients. Here, there are 3 stages of nesting -- tooth surfaces within teeth within patients. Patients are the primary clusters, which are independent of each other. Nested within each patient are teeth and surfaces within teeth. For such a study, you would usually use DESIGN=WR with "NEST _ONE_ PATIENT;". The SUDAAN keyword _ONE_ indicates that there was no stratification or blocks and all patients are in one stratum. Also, the variable PATIENT identifies each patient (the PSU) by taking on a common value for all of the observations from a single patient. The data must be sorted by PATIENT. This is all that SUDAAN needs to know in order to calculate valid variance estimates and hypothesis tests.
Other examples are:
- Teratology studies with pups nested within litters.
- NEST _ONE_ LITTERID;
- Educational studies with students nested within classrooms nested within schools. The schools were stratified by region of the country.
- NEST REGION SCHOOLID;
- A clinical study with longitudinal repeated measurements nested within body sites (e.g., eyes), nested within patients.
- NEST _ONE_ PATIENT;
Yes! Beginning with Release 8.0 you can analyze data with Jackknife weights already computed on the main (or auxiliary) data set. Specify DESIGN=JACKKNIFE on the PROC statement and the Jackknife weights on the JACKWGTS statement. See Chapter 3 of the SUDAAN User’s Manual for details on using this new feature.