Biostatistical Methods II PubH 6415 Spring 2007

2 PubH 6415 – Biostatistics I Instructor: Susan Telke (office hours: lecture hall or by appointment, location -A349 Mayo building) Teaching Assistant: Fang Liu– Katie Schomaker –

3 Books for 6415 Text Book: Introductory Biostatistics-(Chap T. Le) –Wiley SAS Books (highly recommended): The Little SAS Book – Delwiche and Slaughter Applied Statistics and the SAS Programming Language – Cody and Smith

4 Web Page Information on the web: 1. General class information 2. Syllabus 3. Course notes (updated weekly) 4. Homework 5. Computer Help- How to access SAS! 6. In Class Data Sets – More SAS examples

5 Computer Labs Mayo D199 (Classroom & Lab) Mayo D199 (Classroom & Lab) Teaching Assistant will have lab sessions in this classroom before and after Wednesday’s class. Deihl Hall (Medical Library) Deihl Hall (Medical Library) Coffman Union Coffman Union Carlson School of Management Carlson School of Management School of Public Health Lounge (Mayo) School of Public Health Lounge (Mayo)

6 SAS Primary computing environment will be PC SAS PC SAS can be purchased at the bookstore (one year agreement is about $150). PC SAS can be purchased at the bookstore (one year agreement is about $150). OR SAS (not PC SAS) is available using the UNIX version of SAS by SSH to the biostat workstation saturn. Instructions for use on course website. SAS (not PC SAS) is available using the UNIX version of SAS by SSH to the biostat workstation saturn. Instructions for use on course website.

7 Exams and Homework There will be weekly homework assignments There will be weekly homework assignments There will be two midterms and one final exam. There will be two midterms and one final exam. The midterms account for 25% each and the final accounts for 30% of the course grade. The remaining 20% is based on homework (best 10) The midterms account for 25% each and the final accounts for 30% of the course grade. The remaining 20% is based on homework (best 10)

8 Course objectives: Write and run simple SAS programs to perform common analyses. Write and run simple SAS programs to perform common analyses. Analyze health science data using basic statistical and inferential techniques. Analyze health science data using basic statistical and inferential techniques. Understand statistical methods as commonly presented in public health literature Understand statistical methods as commonly presented in public health literature

9 Topics Covered T-tests (review) T-tests (review) One Factor ANOVA/ Two Factor ANOVA One Factor ANOVA/ Two Factor ANOVA Linear regression Linear regression Logistic regression (plus Poisson) Logistic regression (plus Poisson) Survival analyses Survival analyses Proportional Hazards Proportional Hazards Sample Size Determination (If time allows) Sample Size Determination (If time allows) SAS programming to do above analyses

10 SAS Usage SAS is the worlds largest privately held software company SAS is the worlds largest privately held software company 40,000 customer sites worldwide 40,000 customer sites worldwide 3.5 million users worldwide 3.5 million users worldwide 90% of Fortune 500 companies use SAS 90% of Fortune 500 companies use SAS Nearly all analyses of publications in medical research use SAS Nearly all analyses of publications in medical research use SAS SAS invests extensive resources to R & D. SAS invests extensive resources to R & D.

11 What is SAS ? SAS is a programming language that reads, processes, and performs statistical analyses of data. SAS is a programming language that reads, processes, and performs statistical analyses of data. A SAS program is made up of programming statements which SAS interprets to do the above functions. A SAS program is made up of programming statements which SAS interprets to do the above functions.

12 Raw Data Read in Data Process Data (Create new variables) Output Data (Create SAS Dataset) Analyze Data Using Statistical Procedures Data Step PROCs

13 Structure of Data Made up of rows and columns Made up of rows and columns Rows in SAS are called observations Rows in SAS are called observations Columns in SAS are called variables Columns in SAS are called variables An observation is all the information for one entity (patient, patient visit, clinical center, county) SAS processes data one observation at a time

14 Example of Data 12 observations and 5 variables F 23 S 15 MN F 21 S 15 WI F 22 S 09 MN F 35 M 02 MN F 22 M 13 MN F 25 S 13 WI M 20 S 13 MN M 26 M 15 WI M 27 S 05 MN M 23 S 14 IA M 21 S 14 MN M 29 M 15 MN Gender Age Marital status Number of credits State of residence

* This is a short example program to demonstrate what a SAS program looks like. This is a comment statement because it begins with a * and ends with a semi-colon ; DATA demo; INPUT gender $ age marstat $ credits state $ ; if credits > 12 then fulltime = 'Y'; else fulltime = 'N'; if state = 'MN' then resid = 'Y'; else resid = 'N'; DATALINES; F 23 S 15 MN F 21 S 15 WI F 22 S 09 MN F 35 M 02 MN F 22 M 13 MN F 25 S 13 WI M 20 S 13 MN M 26 M 15 WI M 27 S 05 MN M 23 S 14 IA M 21 S 14 MN M 29 M 15 MN ; RUN; TITLE 'Running the Example Program'; PROC PRINT DATA=DEMO ; VAR gender age marstat credits fulltime state ; RUN;

16 Rules for SAS Statements and Variables SAS statements end with a semicolon (;) SAS statements end with a semicolon (;) SAS statements can be entered in lower or uppercase SAS statements can be entered in lower or uppercase Multiple SAS statements can appear on one line Multiple SAS statements can appear on one line A SAS statement can use multiple lines A SAS statement can use multiple lines Variable names can be from 1-32 characters and begin with A-Z or an underscore (_) Variable names can be from 1-32 characters and begin with A-Z or an underscore (_)

DATA demo; Create a SAS dataset called demo INPUT gender $ What are the variables age marstat $ credits state $ ; if credits > 12 then fulltime = 'Y'; else fulltime = 'N'; if state = 'MN' then resid = 'Y'; else resid = 'N'; Last two Statements create 2 new variables(fulltime and state -Character)

DATALINES; Tells SAS the data is coming F 23 S 15 MN F 21 S 15 WI F 22 S 09 MN F 35 M 02 MN F 22 M 13 MN F 25 S 13 WI M 20 S 13 MN M 26 M 15 WI M 27 S 05 MN M 23 S 14 IA M 21 S 14 MN M 29 M 15 MN ; Tells SAS the data is ending RUN; Tells SAS to run the statements

19 Types of Data Numeric (e.g. age, blood pressure) Numeric (e.g. age, blood pressure) Character (patient name, ID, diagnosis) Character (patient name, ID, diagnosis) Each type treated differently by SAS

TITLE 'Running the Example Program'; PROC PRINT DATA=demo ; VAR gender age marstat credits fulltime state ; RUN; * You can run additional procedures; PROC MEANS DATA=demo ; VAR age credits ; RUN; PROC FREQ DATA=demo ; TABLES gender ; RUN;

21 Files Generated When SAS Program is Submitted Log file – a text file listing program statements processed and giving notes, warnings and errors. Log file – a text file listing program statements processed and giving notes, warnings and errors. (in UNIX the file will be named filename.log) Always look at the log file ! Tells how SAS understood your program Output file – a text file giving the output generated from the PROCs Output file – a text file giving the output generated from the PROCs (in UNIX the file will be named filename.lst)

22 Messages in SAS Log Notes – messages that may or may not be important Notes – messages that may or may not be important Warnings – messages that are usually important Warnings – messages that are usually important Errors – fatal in that program will abort Errors – fatal in that program will abort (notes and warnings will not abort your program)

LOG FILE NOTE: Copyright (c) by SAS Institute Inc., Cary, NC, USA. NOTE: SAS (r) Proprietary Software Release 8.2 (TS2M0) Licensed to UNIVERSITY OF MINNESOTA, Site NOTE: This session is executing on the WIN_NT platform. NOTE: SAS initialization used: real time 7.51 seconds cpu time 0.89 seconds 1 * This is a short example program to demonstrate what a 2 SAS program looks like. This is a comment statement because 3 it begins with a * and ends with a semi-colon ; 4 5 DATA demo; 6 INFILE DATALINES; 7 INPUT gender $ age marstat $ credits state $ ; 8 9 if credits > 12 then fulltime = 'Y'; else fulltime = 'N'; 10 if state = 'MN' then resid = 'Y'; else resid = 'N'; 11 DATALINES; NOTE: The data set WORK.DEMO has 12 observations and 7 variables. NOTE: DATA statement used: real time 0.38 seconds cpu time 0.06 seconds

25 RUN; 26 TITLE 'Running the Example Program'; 27 PROC PRINT DATA=demo ; 28 VAR gender age marstat credits fulltime state ; 29 RUN; NOTE: There were 12 observations read from the data set WORK.DEMO. NOTE: PROCEDURE PRINT used: real time 0.19 seconds cpu time 0.02 seconds 30 PROC MEANS DATA=demo N SUM MEAN; 31 VAR age credits ; 32 RUN; NOTE: There were 12 observations read from the data set WORK.DEMO. NOTE: PROCEDURE MEANS used: real time 0.25 seconds cpu time 0.03 seconds 33 PROC FREQ DATA=demo; TABLES gender; 34 RUN; NOTE: There were 12 observations read from the data set WORK.DEMO. NOTE: PROCEDURE FREQ used: real time 0.15 seconds cpu time 0.03 seconds