1. Melanie Dove, MPH, ScD UC Davis
Katherine Heck, MPH
UC San Francisco
QSCERT-PC Postdoc, UCD
Surveys: National Health and Nutrition Examination Survey (NHANES), California Health Interview Survey (CHIS)
Previously: California Department of Public Health, CDC/NCHS
Research analyst, UCSF
Surveys: California Maternal and Infant Health Assessment (MIHA) survey, Listening to Mothers-CA
Previously: California Department of Public Health, CDC/NCHS

2. Survey data analysis made easy with SAS
Melanie Dove, MPH, ScD
UC Davis
Katherine Heck, MPH
UC San Francisco

3. Overview Background Survey design factors (weight and variance)
How to analyze the data
Spend some time on the sampling methods and survey design factors because you need to know a little about the particular survey you’re using in order to analyze the data. We plan to use the California Health Interview Survey and the National Health and Nutrition Examination Surveys as examples.

4. Surveys
Representativeness: Using a sample of individuals to represent a population

5. Survey data Different types: Cross sectional Data collection methods
Health, economic, marketing, sociology, psychology
Cross sectional
Data collection methods
In person, phone, mail, online

6. California Health Interview Survey (CHIS)
Health survey that
represents California
California’s population:
39,809,693 (1/1/2018)
Does anyone know the population of California?
State of California, Department of Finance, E-1 Population Estimates for Cities, Counties and the State with Annual Percent Change — January 1, 2017 and Sacramento, California, May 2018.

7. Sampling
Convenience
Simple random
Stratified

8. Sampling Cluster Stratification
within specified groups or geographic areas
sometimes called primary sampling units (PSUs)
Stratification
select a specified number of individuals from a particular population group
can be used for oversampling 
Cluster example – schools within a district
Stratification example – age group or county
Define oversampling?

9. Stratified Cluster Which one is the stratified sampling scheme?
Stratified
Cluster

10. Variance Individuals within clusters are similar
Overestimate variance – significance
Need to account for the sample design if any stratification, clustering, or weighting was used
If not, result will be incorrect standard errors and confidence intervals
SAS survey procedures allow you to indicate design variables in the syntax
Will correct for these effects 
For example, if counties were used as a cluster, individuals within the same county will be similar to each other.

11. Weighting
Weight: a value indicating the number of people the respondent represents
CA - 39,809,693
CHIS - 24,031
When data are weighted, resulting estimates are representative of the population.
Corrects for:
differing probability of sampling within clusters or strata
nonresponse
Weight

12. Weights Single weight variable -or-
Replicate weights, a series of weight variables which must be used in combination to correctly weight the sample
SAS survey syntax in the weighting statement differs slightly depending on the type of weight used

13. SAS survey procedures SAS survey procedures:
Proc Surveyfreq: Frequencies, crosstabs
Proc Surveymeans: Means, medians
Proc Surveyreg: Linear regression
Proc Surveylogistic: Logistic regression
Proc Surveyphreg: Cox proportional hazards model
Proc Surveyselect: Sample selection
Procedures can produce standard errors and confidence intervals
Variables may be continuous or categorical. There are many test statistics you can produce with these, such as chi-square, t-tests, and so forth .

14. Results with and without survey procedures: confidence intervals
Example: CHIS, 2016 adult survey
Weighted percent and confidence interval
* Ever diagnosed with asthma, age 30-34
Proc Freq results: % (13.85%-13.93%)
Proc Surveyfreq results: 13.89% (9.97%-17.80%)
What do you notice about the percentages and the 95% confidence intervals?

15. Survey components and syntax
Stratification: STRATA statement
Clustering: CLUSTER statement
Weighting:
Subpopulation analyses: DOMAIN statement or “flag” variables 
Do not use “where” to subset data
WEIGHT statement (and REPWEIGHT if using replicate weights)
What do you think the weighting statement is called?
This is a short workshop, but we want to let you know that there is also syntax to incorporate something called the Finite Population Correction, which makes a minor adjustment of the standard error based on the fraction of the population that is sampled; some survey data sets include this.

16. Survey procedure examples

17. Proc Surveyfreq - stratum/cluster
proc surveyfreq data=dataset varmethod=taylor;
strata  stratum;
cluster PSU;
weight  weightvar;
tables  agegrp;
run;
proc freq data=dataset;
tables agegrp;
run;
Basic code. Varmethod = taylor is the default, so don’t actually need this statement. But, need to know what the variance method is.

18. Proc Surveyfreq - stratum/cluster
Missing data
proc surveyfreq nomcar data=dataset total=c.sampfrac;
strata  stratum;
cluster PSU;
weight  weightvar;
tables  agegrp * disease / row col cl;
format  agegrp agegrpf.;
run;
Finite pop
correction
Code with extras.
Confidence
limits
Row %
Col %

19. Proc Surveyfreq - replicate weights
proc surveyfreq data=dataset varmethod=jackknife;
weight  weightvar;
repweight  wtvar1-wtvar80/JKCOEFS=1;
tables  agegrp * disease / row cl ;
format  agegrp agegrpf.;
run;
Variance estimation method
Two weighting statements
Is there a statement that appears to be missing from this SAS code? Stratum information is contained in the replicate weights, so you don’t need a separate ‘Strata’ statement if you use the replicate weights. CHIS doesn’t use clustering, so you don’t need a ‘cluster’ statement.

20. Libname statement data adult; run; libname CHIS ‘C:\HOW\Heck’;
set chis.adult;
run;
Example with NHANES using NHANES variables- actually run in SAS?

21. CHIS age variable

22. Proc Surveyfreq - age proc surveyfreq data=adult varmethod= ?????;
weight   ????? ;
repweight   ????? /JKCOEFS=1;
tables  ?????  ;
run;
Jackknife variance method
* Weight variable = rakedw0
Repweight = rakedw1-rakedw80
Age = srage_p1

23. Proc Surveyfreq - age proc surveyfreq data=adult varmethod=jackknife;
weight  rakedw0;
repweight  rakedw1-rakedw80/JKCOEFS=1;
tables  srage_p1 / cl ;
run;

24. Proc Surveyfreq - Results

25. CHIS: Asthma variable

26. Proc Surveyfreq syntax
proc surveyfreq data=adult varmethod=jackknife;
weight   ?????;
repweight   ????? / JKCOEFS=1 ;
tables  ????? * ?????  / row cl nototal ;
run;
Category
(age)
Outcome
(asthma)
No row/col
totals

27. Proc Surveyfreq syntax
proc surveyfreq data=c.adult varmethod=jackknife;
weight  rakedw0;
repweight  rakedw1-rakedw80 / JKCOEFS=1 ;
tables  srage_p1 * ab17 / row cl nototal ;
run;
Category
(age)
Outcome
(asthma)
No row/col
totals

28. Proc Surveyfreq output

29. Proc Surveyfreq with chi-square
proc surveyfreq data=c.adult varmethod=jackknife;
weight  weightvar;
repweight  wtvar1-wtvar80 / JKCOEFS=1 ;
tables  srsex * ab29 / row cl nototal chisq ;
run;
Gender
Hypertension
Chi-square

30. Proc Surveyfreq output

31. Proc Surveymeans example CHIS 2016, number of times walked for leisure, past 7 days, by family type 
proc surveymeans data=c.adult varmethod=JACKKNIFE; 
weight    rakedw0;
repweight rakedw1-rakedw80 / JKCOEFS=1 ;
var       AD41W ;
domain    FAMT4 ;
run;
Can also add the ‘class’ statement to proc surveymeans.
AD41W = how often walked
Domain = group(s) of interest
FAMT4 = family structure

32. Results

33. Proc Surveylogistic example Usual source of care by uninsured, adults 18-64, CHIS 2016
proc surveylogistic data=adult varmethod=JACKKNIFE; 
weight    rakedw0;
repweight rakedw1-rakedw80/JKCOEFS=1;
class     uninsured (ref='Insured');
model     nousual (descending) = uninsured ;
format    uninsured unins.;
run;
We created a variable for having no usual place where the respondent obtained health care when they need it. The logistic outcome variable is coded 1 if they had no usual source of care, or 0 if they had a usual source.

34. Proc Surveylogistic results

35. Resources to analyze CHIS data
Analyze CHIS Data website: spx Webinar: chis-data-analysis-webinar-recording/

36. Thank you!
Questions?

37. Contact Information
Name: Melanie Dove Company: UC Davis City/State: Sacramento, CA Phone:

38. Contact Information
Name: Katherine Heck Company: UCSF City/State: San Francisco, CA Phone: