Introduction to Statistical Computing in Clinical Research

Biostatistics 212

Lecture 1

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

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

Course details

• Biostats 212

– 1 Unit Course– Satisfactory/Unsatisfactory vs. Grades– 7 Sessions – Lecture + Lab, starting August 2

Course details

• New this year:

– In-Person + Online versions of the course

– Recorded lectures

– Forum

Course details

• Two “In-Person” Sections:

– Lectures – in person (6702), Tuesday 1:15-2:45

– Labs – in person (6702 + 6704), Tuesday 3:00-4:00

Course details

• One “Online” Section:

– Lectures – Recorded, posted late Tuesday afternoon

– Labs – Online Wed 1:30-3:00• New this year, online students only

• Led by Jen Cocohoba – comments?

Course details

• Recorded Lectures

– Audio + video of lecturer + video of screen

– Available same day for viewing

– See http://xxxxxxxxxxxxxxx

Course details

• Forum

– Demo

– Post all questions here!• TA turnaround time

– Before you post, see if it’s already there and answered

– Consider turning ON your alerts around lab time…

Course details

• Course Requirements– Hand in all six Labs (even if late)

– Satisfactory Final Project

• Not required– Reading

– Attendance

Course details

• Grading (only relevant for Master’s and ATCR Credit-Bearing students?)

– Letter grades: Standard cutoffs• 90-100% A

• 80-89% B

• 70-79% C

• 60-69% D

• <60% or Course Requirements not met: F

– Satisfactory/Unsatisfactory• >80%

Satisfactory

Course details, cont

Course Director

Mark Pletcher

TA’s

Naomi Bardach

Raymond Hsu

Sharon Poisson

Monika Sarkar

Assistant Course DirectorJennifer Cocohoba

Lab InstructorsJing ChengBarbara GrimesNancy HillsAnn Lazar

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 tables and figures with STATA

• 7- Advanced Programming Topics

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 In-Person Lab time is 3:00-4:00 on Tuesday, but we will start right after lecture, and you can leave when you are done.

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 (Elizabeth/Raman) or biostat212_section2@yahoo.com (David/Yvette)

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

• Online Syllabus (http://www.epibiostat.ucsf.edu/courses/schedule/biostat212.html)

– Lectures and Labs/Datasets (“just in time”)– Miscellaneous handouts– Final Project

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 is free and 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

Demo #1

• Open the program

• Entering vs. loading data

• Look at data

• Run a command

• Orient to windows and buttons

STATA - Windows

• Two basic windows– Command

– Results

• Optional windows– Variable list

– Properties

– History of commands

• Other functions– Data browser/editor

– Variables Manager

– 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

Menu vs. Command line

• 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

Demo #2

• Load a STATA dataset

• Explore the data

• Describe the data

• Answer some simple research questions– Gender, BMI, blood pressure

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

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.

STATA commandsGraphical 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 commandsDescriptive statistics – categorical data

• tabulate [varname]– Counts and percentages

– (see also, table - this is very different!)

STATA commandsAnalytic statistics – 2 categorical variables

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)

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 commandsAnalytic statistics – 1 categorical, 1 continuous

STATA commandsAnalytic 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 commandsAnalytic statistics – 2 continuous

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)]

In Lab Today…

• Expect some chaos!– IT will be here to help with wireless, logins, etc

• 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– 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 Server)– http://www.microsoft.com/mac/remote-desktop-client

• Citrix Web Server– http://apps.epi-ucsf.org/

• Stata 12 Server– 65.175.48.75