PubH 6470 introduces students with a background in statistics to programming, graphics, and data analysis using SAS. The course concentrates on data-step programming, data editing and reformatting, as well as statistical applications. Students will complete and present a data analysis for a final class project, using data from the student's field if possible. Projects need advance approval from the instuctor.

Instructor: William Thomas, Mayo A-467, 625-0651
Office hours: 2:30 - 3:30 Wednesdays or by appointment

In addition to Mayo C-381, PC-SAS is installed at these computing labs:
Diehl Hall Biomed Library
Coffman Union

Many documents on this website are in PDF format. For the software to read them, download Adobe Acrobat Reader here.

I recommend doing the coursework with PC-SAS on your own computer.
Get PC-SAS through the University of Minnesota for $120 per year.

• UCLA SAS Help Page: examples, videos
• Stanford SSDS Getting Started Guides
• Phil Spector's Intro to SAS (slides)
• CSCAR Univ of Michigan SAS Help
• Yale Statlab SAS Help
• Univ of Texas SAS Help

• SAS Help documentation
• Google
• SAS customer support: code samples, search for papers presented at SAS Users Group conferences
• SAS L-archives search: Q & A
• UCLA SAS Help Page: advanced examples
• SAS Press: code and data from SAS books

• HW 1, HW1 NHANES3 Adult spreadsheet, HW1 NHANES3 Exam spreadsheet, Child IQ.xls, example demographics table.

• HW 2, hw2-demographics.xls, hw2-visits.xls, OGTT.xls.

• HW 3, Florida 2000.xls, clinical.xls.

• Midterm, MinnHeart.xls.

• HW 4, HW4 NHANES3 exam.xls.

• HW 5, Collection Dates.xls, Alzheimer.xls.

• Intro to SAS: reading and storing data, linear regression (Weeks 1-2)
• 1. Intro to SAS I; Child IQ.xls, SAS program
• 2. Intro to SAS II; current version of our SAS program, UCLA handout on centering variables
• 3. Data checking; centering predictors program,
• 4. Tests, missing values, SET and MERGE.
Reading
• Little SAS Book Sections 1.1-1.4,1.6-1.9, 1.11-1.13, 2.3
• Basics: DATA step, arrays, plots, ANOVA and regression, LSmeans, macros, bootstrap (Weeks 3-8)
• 5. Data set options, graphics, more on set and merge, scatterplots program.
• 6. Design matrix, Reg and GLM, discrepant_values.sas.
• 7. More on indicator variables, Reg and GLM, date and time data, HAMD.xls.
• 8. Interaction plots, dates, arrays, computing AUC, SAS code, HAMD.xls, barley.xls, blood samples.xls.
• 10. Macros.
• 12. Linear model example: Minnesota math scores, SAS code, grade8_mathscores.xls.
• 13. Character variables, program structure , updated SAS code.
• 14. Continue regression example: outliers , updated SAS regression code.
• 15. Finish regression example, ODS select and ODS output, updated SAS regression code.
• 16. Bootstrap: macros and Proc Surveyselect, SAS bootstrap code, bootstrapmacros.sas, SAS bootstrap documentation.
• 17. Reading a spreadsheet with problems, bad-spreadsheet.xls, bad-spreadsheet_1.csv, bad-spreadsheet_2.csv, Class17.sas.
• Take-home midterm (22 October), MinnHeart.xls.
• Longitudinal data, crossover trials, hierarchical linear models (Weeks 8-10)
• 18. Longitudinal data I, SAS code, Alzheimer.xls.
• 19. Longitudinal data II, SAS code.
• 20. Random effects, multilevel models, SAS code.
• 21. Crossover trials.
• Logistic regression: prediction, propensity scores, matching, ordinal regression (Weeks 11-12)
• Class presentations (Weeks 13-15)

2008 Lecture Notes and Examples

1. Intro to SAS I; SAS program.
2. Intro to SAS II, reading Excel spreadsheets; Workbook1.xls, Workbook2.xls, SAS program.
3. Data checking, Proc Insight, SAS Manual, basic tests
4. Missing values, graphics, reporting in MSWord, SET, MERGE; "Fix SAS output" MSWord macro, SAS program
5. Merging, data set options, GLM.
6. GLM: residual & interaction plots, means, LSmeans, dates, arrays; SAS program.
7. GLM: LSmeans, estimate; missing values.
8. MI and MIanalyze with GLM; smoothing, jitter; SAS code, HAMD2 data.
9. Correlation, partial correlation, regression: Proc REG; SAS program, Grade 8 data (SAS permanent file).
10. Regression example, VIF, plots, subset selection.
11. Making CLASS variables for Proc Reg, predictions, sample size; SAS program.
12. Macros and Bootstrap; SAS program, bootstrapmacros.sas, SAS bootstrap documentation.
13. Bootstrap confidence intervals: correlation, kappa, agreeement.
14. Bootstrap prediction error, t-tests.
15. Longitudinal data: graphs, area under a curve (AUC); SAS program.
16. Within-person correlation, covariance matrix.
17. Proc Mixed: repeated measures, random effects.
18. Crossover designs.
19. Logistic regression.
20. Log-binomial, repeated binary observations.
21. Conditional logistic regression, ordinal logistic regression.
22. Survival data, Kaplan-Meier estimates, randomization log-rank test, SAS code, macro file from Cantor: SAS Survival Analysis Techniques, 2nd ed..
23. Reporting comparisons of survival curves, proportional hazards regression.
24. Checking proportional hazards, subset selection, time-varying predictors.
25. Competing risks: cumulative incidence, SAS code for lecture, CumIncid macro (from www.sas.com), BMT data from Klein & Moeschberger, Survival Analysis,2nd ed, sec 1.3, App D.