Introduction to Statistical Computing in Clinical Research

1. Introduction to Statistical Computing in Clinical Research Biostatistics 212 Lecture 1

2. Today... • Course overview • Course objectives • Course details: grading, homework, etc • Schedule, lecture overview • Where does Stata fit in? • Basic data analysis with Stata • Stata demos • Lab

3. Course Objectives • Introduce you to using STATA and Excel for • Data management • Basic statistical and epidemiologic analysis • Turning raw data into presentable tables, figures and other research • Prepare you for Fall courses • Start analyzing your own data

4. Course details Introduction to Statistical Computing - 1 unit Schedule – 7 lectures, 7 lab sessions, on 7 Tuesdays in a row Dates: July 31, August 7,14,21,28, September 4,11 Lectures 1:15-2:45 Labs 3:00-4:00 All in China Basin (CBL 6702, 6704) Final Project Due 9/18/06

5. Course details Introduction to Statistical Computing Grading: Satisfactory/Unsatisfactory Requirements: -Hand in all five Labs (even if late) -Satisfactory Final Project -80% of total points Reading: Optional

6. Course Director Mark Pletcher, 514-8008 mpletcher@epi.ucsf.edu Teaching Assistants Jennifer Cocohoba jcocohoba@nccc.ucsf.edu biostat212_section1@yahoo.com Sharon Chung sharon.chung@ucsf.edu biostat212_section2@yahoo.com Folashade Jose josef@peds.ucsf.edu Lab Instructors Mandana Khalili Alan Bostrom Course details, cont

7. Overview of lecture topics • 1- Introduction to STATA • 2- Do files, log files, and workflow in STATA • 3- Generating variables and manipulating data with STATA • 4- Using Excel • 5- Basic epidemiologic analysis with STATA • 6- Organizing a project, making a table • 7- Making a figure with STATA or Excel

8. Overview of labs • Lab 1 – Load a dataset and analyze it • Lab 2 – Learn how to use do and log files • Lab 3* – Import data from excel, generate new variables and manipulate data, document everything with do and log files. • Lab 4 – Using and creating Excel spreadsheets • Lab 5* – Epidemiologic analysis using Stata Last 2 lab sessions dedicated to working on the Final Project * - Labs 3 and 5 are significantly longer and harder than the others

9. Overview of labs, cont • Official Lab time is 3:00-4:00, but we will start right after lecture, and you can leave when you are done. • Lab sections led by Jennifer Cocohoba and Sharon Chung • “Mac session” • Labs also staffed by Fola Jose, Alan Bostrom, Mandana Khalili, and I

10. Overview of labs, cont • Labs are due the following week prior to lecture. Labs turned in late (less than 1 week) will receive only half credit; after that, no points will be awarded. However, ALL labs must be turned in to pass the class (even if no points are awarded). • Lab 1 is paper • Labs 2-5 are electronic files, and should be emailed to your section leader’s course email address: biostat212_section1@yahoo.com (Jennifer) or biostat212_section2@yahoo.com (Sharon)

11. Final Project • Create a Table and a Figure using your own data, document analysis using Stata. • Due 1 week after last lab session, 20 points docked for each 1 day late.

12. Orientation to binder • Course Overview • Final Project • Lectures and Labs (just in time) • Other handouts

13. Getting started with STATA Session 1

14. Types of software packages used in clinical research • Statistical analysis packages • Spreadsheets • Database programs • Custom applications • Cost-effectiveness analysis (TreeAge, etc) • Survey analysis (SUDAAN, etc)

15. Software packages for analyzing data • STATA • SAS • S-plus, and “R” • SPS-S • SUDAAN • Epi-Info • JMP • MatLab • StatExact

16. Why use STATA? • Quick start, user friendly • Immediate results, response • You can look at the data • Menu-driven option • Good graphics • Log and do files • Good manuals, help menu

17. Why NOT use STATA? • SAS is used more often? • SAS does some things STATA does not • Programming easier with S-plus? • Complicated data structure and manipulation easier with SAS? • Epi-info (free) is even easier than STATA?

18. STATA – Basic functionality • Holds data for you • Stata holds 1 “flat” file dataset only (.dta file) • Listens to what you want • Type a command, press enter • Does stuff • Statistics, data manipulation, etc • Shows you the results • Results window

19. Demo #1 • Open the program • Load some data • Look at it • Run a command

20. Two basic windows Command Results Optional windows Variable list History of commands Other functions Data browser/editor Do file editor Viewer (for log, help files, etc) STATA - Windows

21. STATA - Buttons • The usual – open, save, print • Log-file open/suspend/close • Do-file editor • Browse and Edit • Break

22. STATA - Menus • Almost every command can be accessed via menu

23. Demo #2 • Enter in some data • Look at it • Run a couple of commands

24. Menu advantages Look for commands you don’t know about See the options for each command Complex commands easier – learn syntax Command line advantages Faster (if you know the command!) “Closer” to the program Only way to write “do” files Document and repeat analyses Menu vs. Command line

25. STATA commandsDescribing your data • describe [varlist] • Displays variable names, types, labels • list [varlist] • Displays the values of all observations • codebook [varlist] • Displays labels and codes for all variables

26. STATA commandsDescriptive statistics – continuous data • summarize [varlist] [, detail] • # obs, mean, SD, range • “, detail” gets you more detail (median, etc) • ci [varlist] • Mean, standard error of mean, and confidence intervals • Actually works for dichotomous variables, too.

27. STATA commandsGraphical exploration – continuous data • histogramvarname • Simple histogram of your variable • graph box varlist • Box plot of your variable • qnorm varname • Quantile plot of your variable to check normality

28. STATA commandsDescriptive statistics – categorical data • tabulate [varname] • Counts and percentages • (see also, table - this is very different!)

29. STATA commandsAnalytic statistics – 2 categorical variables

30. STATA commandsAnalytic statistics – 2 categorical variables • tabulate [var1] [var2] • “Cross-tab” • Descriptive options , row (row percentages) , col (column percentages) • Statistics options , chi2 (chi2 test) , exact (fisher’s exact test)

31. Getting help • Try to find the command on the pull-down menus • Help menu • If you don’t know the command - Search... • If you know the command - Stata command... • Try the manuals • more detail, theoretical underpinnings, etc

32. STATA commandsAnalytic statistics – 1 categorical, 1 continuous

33. STATA commandsAnalytic statistics – 1 categorical, 1 continuous • bysortcatvar: summarize [contvar] • mean, SD, range of one in subgroup • ttest [contvar], by(catvar) • t-test • oneway [contvar] [catvar] • ANOVA • table [catvar] [, contents(mean [contvar]…) • Table of statistics

34. STATA commandsAnalytic statistics – 2 continuous

35. STATA commandsAnalytic statistics – 2 continuous • scatter [var1] [var2] • Scatterplot of the two variables • pwcorr [varlist] [, sig] • Pairwise correlations between variables • “sig” option gives p-values • spearman [varlist] [, stats(rho p)]

36. Demo #3 • Load a STATA dataset • Explore the data • Describe the data • Answer some simple research questions

37. In Lab Today… • Familiarize yourself with Stata • Load a dataset • Use Stata commands to analyze data and fill in the blanks

38. Next week • Do files, log files, and workflow in Stata • Find a dataset!