Introduction to Statistical Computing in Clinical Research Biostatistics 212 Lecture 1 Good afternoon, and welcome! This is the first day of class for Biostatistics 212, and, I believe, your first time together as Advanced Clinical Research Scholars (including both ATCR and Masters Program scholars).

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

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 products Prepare you for Fall courses Start analyzing your own data Objective 1 – you may have your own data; we supply STATA; here’s how to start using it Objective 2 – a response to complaints about the Glantz course 2 options – spend more time teaching you how to do his homework - or teach you what we think you’ll need to know Objective 3 – The idea here is to teach you how to do clinical research. What does that mean? Use data to make products! Knowing how to type the right STATA command is a necessary skill, but you also need to know how to manage data, clean it, document your analyses, export data, and create products that you want other people to understand – i.e. publish. In the future, you may not be the one to do the data analysis, but even so, it’s important for you to understand how it works, to make you a self-sufficient clinical researcher.

Course details Introduction to Statistical Computing - 1 unit Schedule – 7 lectures, 7 lab sessions, on 7 Thursdays 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 for lab) Final Project Due 9/18/06

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

Course details, cont Course Director Mark Pletcher, 514-8008 mpletcher@epi.ucsf.edu Teaching Assistants Jennifer Cocohoba cocohobaj@pharmacy.ucsf.edu biostat212_section1@yahoo.com Phil Blumenshine BlumenshineP@fcm.ucsf.edu biostat212_section2@yahoo.com Lab Instructors Carolyn Calfee Barbara Grimes Mandana Khalili Lecturer Andy Choi

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- Making a figure with STATA 7- Organizing a project, making a table

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 Lab 6 – Making a figure with Stata Last lab session will be dedicated to working on the Final Project * - Labs 3 and 5 are significantly longer and harder than the others

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 Phil Blumenshine All Mac users in Section 2 Labs also staffed by Carolyn Calfee, Andy Choi, Barbara Grimes, Mandana Khalili, and I

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 2-6 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 (Phil)

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.

Course Materials No binder this year! Course Overview Final Project Lectures and Labs (just in time) Other handouts

Getting started with STATA Session 1

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)

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

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

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

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 Do a quick demo – open STATA, load a dataset, look at the data, run summarize on age.

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

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

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

STATA - Menus Almost every command can be accessed via menu

Demo #2 Enter in some data Look at it Run a couple of commands Three nonsense variables – 2 numeric, 1 string – Save as…. Describe, Tablulate, Summarize, List Use menus for Tabulate Review commands Look at variable list

Menu vs. Command line Menu advantages Command line 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

STATA commands Describing 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

STATA commands Descriptive 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.

STATA commands Graphical exploration – continuous data histogram varname Simple histogram of your variable graph box varlist Box plot of your variable qnorm varname Quantile plot of your variable to check normality

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

STATA commands Analytic statistics – 2 categorical variables

STATA commands Analytic 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)

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

STATA commands Analytic statistics – 1 categorical, 1 continuous

STATA commands Analytic statistics – 1 categorical, 1 continuous bysort catvar: 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

STATA commands Analytic statistics – 2 continuous

STATA commands Analytic 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)]

Demo #3 Load a STATA dataset Explore the data Describe the data Answer some simple research questions Gender and HTN, age and HTN

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

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

Website addresses Course website Computing information http://www.epibiostat.ucsf.edu/courses/schedule/biostat212.html Computing information http://www.epibiostat.ucsf.edu/courses/ChinaBasinLocation.html#computing Download RDP for Macs (for Stata 10 Server) http://www.microsoft.com/mac/otherproducts/otherproducts.aspx?pid=remotedesktopclient Citrix Web Server http://apps.epi-ucsf.org/