sas healthcare workshop 18 nov 2011

Copyright © 2011, SAS Institute Inc. All rights reserved.

SAS Healthcare Workshop 201118 November 2011

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SAS Healthcare Workshop 2011

Agenda

2.00 - 4.00pm : Statistical Analysis in Healthcare Industry showcasing SAS Enterprise Guide and SAS STAT capabilities in HealthCare analytics - Giuseppe Manai , SAS Senior Pre-sales Consultant- Jun Yao, SAS Pre-sales Consultant

4.00 - 4.15pm : Tea Break

4.15 - 5.15pm : Powerful Features of SAS: Reporting and Presenting Relevant Statistical Summary of Clinical Data - Dr Pryseley N. Assam, Adjunct Assistant Professor for Centre for Quantitative Medicine, Duke-NUS Graduate Medical School & Biostatistician at Singapore Clinical Research Institute

5.15 - 5.45pm : Application of PROC Frequency in Clinical Analysis - Mr Gabriel Jiang, Senior Statistician-Computation, Lilly-NUS Centre for Clinical Pharmacology Pte. Ltd.

5.45 - 6.00pm : Q&A and Closing

Copyright © 2011, SAS Institute Inc. All rights reserved.Copyright © 2011, SAS Institute Inc. All rights reserved.

Statistical Analysis inHealthcare IndustryGiuseppe Manai, SAS Senior Pre-sales Consultant

Jun Yao, SAS Pre-sales Consultant

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Outline

SAS Business Analytics Framework Giuseppe

Introduction to SAS Enterprise Guide SAS Enterprise Guide Workspace

Tasks and Wizards: Easy in Enterprise Guide

Examples + Hands-on Activity

Statistical techniques

Scheduling Jun Yao

What’s New in SAS Enterprise Guide 5.1

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6



How do we do it?

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BUSINESS SOLUTIONS

How do we do it?

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INFORMATION MANAGEMENT

BUSINESS SOLUTIONS

How do we do it?

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BUSINESS SOLUTIONS INFORMATION MANAGEMENT

ANALYTICS

How do we do it?

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REPORTING

BUSINESS SOLUTIONS INFORMATION MANAGEMENT ANALYTICS

How do we do it?

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Healthcare Providers Using SAS

Business IssuesFacing problem in data integration and data analysis. Different software was being used in each department making consolidation of the data difficult to achieve or even visualise.

SolutionIntegration of the clinical and medical management data analysis.

ResultsPatient response times are much quicker and effective.

Giuseppe Manai

would this not involve DI Studio more than EG? EG would be more for data analysis and reporting rather than integration

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Healthcare Providers Using SAS

Business Issues Large data analysis was slow and difficult. Separate software was being used in different departments causing problems with data integration and resulting in analytical limitations.

SolutionIntegrates the different departments under a single repository of information enabling access to all in a standardised single version of the truth.

Results standardized records and reports

http://www.samsunghospital.com/global/en/main/main.do

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Introduction to SAS Enterprise Guide

Giuseppe Manai

too much textr no visual

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Introduction to Enterprise Guide

Quick access and manipulation of data

Carry out basic and complex analysis

Easy to use basic reporting

Easy to use Quick

Intuitive

Giuseppe Manai


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Introduction to Enterprise Guide

What does SAS EG do? Point-and-click GUI

Menu- and wizard- driven

Enables fast data analysis and results publishing

Fast-track learning data analysis

Generates code in background for productivity

Deploys analyses and forecasts in real time.

Who is SAS EG designed for? Business analysts with no SAS programming experience

SAS programmers

Statisticians

Giuseppe Manai


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Introduction to SAS Enterprise GuidePoint-and-click interface for managing data and generating reports.

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Introduction to SAS Enterprise Guide

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Introduction to Enterprise GuideProgramming Interface

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SAS Enterprise Guide Interface

Data sources

SAS programs and logs

Tasks and Queries

Results

Informational Notes for Documentation

You can control contents, sequencing, and updating of a project.

A PROJECT is a single file that serves as a collection of:

Giuseppe Manai

explain the three working spaces in the windowProject treeProcess flow (multiple)Server list

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SAS Enterprise Guide Interface

Giuseppe Manai

explain the three working spaces in the windowProject treeProcess flow (multiple)Server list

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Behind the ScenesAs you build tasks, Enterprise Guide generates SAS code.

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Tasks and Wizards: Easy in EG

Data Management Quickly Visualize, Open or Import Data

Join Datasets efficiently

Create Frequency Reports

Program Editor

Visualization Graphs (Histogram/ Bar-Chart/Pie Chart)

Statistical techniques t-test

ANOVA

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Examples

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Building a SAS Enterprise Guide Project

1. Add Datasets (can also import from Excel)

2. Perform a task

3. One-way frequencies and statistical tests

4. Automate a Process

5. Graphing

6. Create Reports (List Data)

7. Summary Statistics

8. Simple Linear Regression

9. t-Test and ANOVA

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Common Data Formats

SAS datasets

Microsoft Excel and Access

DBMS tables

Text files

JMP

HTML

SPSS

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One-Way Frequency Report

The One-Way Frequencies task can be used to generate: Frequency counts Percentages Graphs Statistical tests to analyze the distribution of your data

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Types of Graphs

The Graph menu

includes all of the

advanced graph tasks

as well as the wizards.

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Graphs Using SAS Enterprise Guide

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Characterize Data

The Characterize Data task is an exploratory

task that produces automatically statistics and

graphs for all variables, grouping by type of

variable (i.e. categorical, interval, etc.)

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Summary Statistics Report

Calculate summary statistics based on groups within the data.

Can produce reports, graphs and data sets as output.

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Automate a Process

To automate a process flow you simply name it

Autoexec

This will run the process flow when opening the project

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Hands-on activity (10-15 min)

Try to: Import a dataset

Create a task

Graph the results

Automate the process

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Simple Linear Regression Analysis

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Simple Linear Regression Analysis

The objectives of simple linear regression are to:

Assess the significance of the predictor variable

in explaining the variability or behavior of the response

variable

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Simple Linear Regression Model

b0

1 unit

b1 units

b 0 + b 1

X

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Simple Linear Regression Model

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Performing Simple Linear Regression

Example ……

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t-Test and ANOVA

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Performing t-Test (Paired Sample)

Analyze differences between two population means using the t-Test task.

Perform a paired-sample t-test to determine

whether a medication was successful in reducing

blood pressure.

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Scenario 1

DatasetTo observe the effectiveness of a medication in reducing blood pressure, an experiment was conducted in which researchers collected data from a random sample of individuals who were considered to have high blood pressure. The diastolic blood pressure of these individuals was recorded, after which they were placed on the medication. One month later, their diastolic pressure was recorded again.

ObjectiveDetermine if the data gives good evidence that the medication was effective in reducing blood pressure by carrying out a test of significance (at level α = 0.05), pairing the two blood pressure recordings for each subject.

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Performing Simple Linear Regression

Example ……

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Analysis of Variance (One-Way ANOVA)

Perform a one-way ANOVA to determine whether

there are significant differences between four drugs

in mean increase in systolic blood pressure.

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Scenario 2

DatasetConsider a pharmaceutical company that has recently focused on the effect of its potential products on blood pressure. Researchers conducted a study in which the subjects were 72 individuals with one of three diseases. Eighteen individuals were randomly assigned to each of the four drugs. The treatments were administered over time and the increase in systolic blood pressure was recorded.

ObjectiveDetermine whether there are significant differences between the mean increases in systolic blood pressure for the four drugs. If there are, evaluate which drugs differ and how greatly they differ.

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One-Way ANOVA

Example…….

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Hands-on activity (10-15 min)

Explore Enterprise Guide’s tasks

Use data sets provided

Modify variables

Use different tasks

Create graphs and reports

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Task Scheduling via Enterprise Guide

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SAS® Enterprise Guide 4.3

Release Objectives User productivity Integration Performance

Key Features New program editor with auto complete and integrated syntax help Enhanced OLAP Analysis capabilities for enriched user experience Tighter integration with SAS WRS Reports and JMP Rapid Predictive Modeling (RPM) Enterprise Miner integration New performance enhancement capabilities with Explicit SQL pass-

through option, In-database optimized syntax generation and Integration for Grid enablement

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Enhancements for SAS Programmers

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Enhanced Program Editor

Mouse over Tooltip with details for SAS PROCS, SAS Functions

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Enhanced Program Editor - Autocomplete

Connects to data library listing accessible data tables

Provides list of SAS PROCS, SAS Functions and Macro variables, assisting user while coding

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Enhanced Program Editor

Converts code into Indented Format

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Enhanced Program Editor – Analyze Program

Coverts lines of Code into Process Flow for visual representation and ease understanding of the program flow

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Adhoc Analysis Enhancements

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OLAP Analyzer – Enhanced Filtering options

Enhanced filter options with Example support

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OLAP Analyzer - Enhanced MDX Editor

• Drag-n-Drop support to add measures/dimensions to the MDX

• Show Line numbers for MDX

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OLAP Analyzer - Enhanced MDX Editor

New options for Search

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OLAP Analyzer - New Chart Types

New Chart Types• Scatter Plot• Tile Chart

• Automatic Charting (creates a chart based on Dimensions and Measures selected in crosstab)

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OLAP Analyzer – Enhanced Explorer View

Apply Path to Siblings option for a smoother data exploration

experience.

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Integration with JMPData opened in EG 4.3

Data opened in JMP

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Integration with Rapid Predictive Modeler

Self service analysis and report generation using wizard driven RPM task

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SAS® Enterprise Guide 5.1

Release Objectives Leverage the capabilities of SAS 9.3 platform Enriched User Experience Increased productivity

Key Features Enhanced program editor with auto complete for dataset

variables Improved tooltip support for SAS keywords with help

documents Enhanced OLAP Analysis capabilities for enriched user

experience Reusable queries (Query Templates and Sub Queries) Enriched interface for creating Stored Process

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Enhanced Program EditorEnriched user experience and increased productivity

Provides the ability to view the list of variables from the selected data set.

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Enhanced tool tip support with web link to Product Documentation and Samples.

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Ability to split and stack the programming window – side by side or tiled

• Work with different parts of same program in single view

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Adhoc OLAP AnalysisEnriched user experience

Predefined Calculation Functions for time-series data analysis

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Adhoc OLAP AnalysisEnriched user experience

Refined Contextual Menu for defining Totals and % of Totals

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Reusable Queries

1 Define queries and save them as Templates

2 Use existing queries as Sub-Queries to filter data

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Options for Code Submissions

Ability to define the Code Submission options and gain

increased performance in a grid enabled environment

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Any Questions?

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Tea-Break


Powerful SAS Features: Reporting & Presenting Statistical Summary of Clinical DataPryseley Assam (PhD)Biostatistician, Singapore Clinical Research InstituteAssistant Professor, Duke-NUS Graduate Medical School

SAS Healthcare Workshop

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Outline

Introduction

A case study – retrospective study

Some useful SAS features (summaries, reports)

Remarks

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Introduction

Mandatory Clinical Trial Registration

» The International Committee of Medical Journal Editors

» Pharmaceutical Research & Manufacturers of America

New England Journal of Medicine (Sept 16 issue 2004)

… patients have a right to trust that investigators will not

only minimize risks to the participants but also will

conduct research ethically and report it completely and

honestly.

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Introduction

Obligation to report/publish research findings

» Participants (patients)

» Scientific committee

» Sponsors

Guidelines for reporting

» Randomized controlled trials: CONSORT

» Systematic reviews and meta-analysis: PRISMA

» Observations studies: STROBE

» Diagnostic studies: STARD

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Introduction

Complete and honest reporting is facilitated by appropriate study design, conduct and analysis, in accordance with the study objectives

Increasing demand for statistical input in

healthcare studies

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Introduction

Statistical report

» Design, size, and outcomes of the study

» Patient selection flow chart

» Summary statistics

» Graphs

» Analysis

Automated summaries and report attributes

Statistical data review

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Case Study - DUMIN

Motivation

» RCTs are not representative of practice

» Strict measurement based-care approach

» Higher doses than found in typical clinical care

Data description

» All outpatients with clinical depression (and anxiety)

» On one antidepressant medication for at least 1week

» At least two assessment within the 9 weeks of treatment

» Study period: January 2000 to December 2010

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Case Study - DUMIN

Objectives

Describe overall response rates

» CGI-I

» CGI-S change from baseline

Is outcome related to

» Baseline severity

» Duration of treatment

Identify clinical features associated with outcomes


Some useful SAS Features

Summarizing Data

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Summarizing Data

Listings

» Useful for providing detail reports; a detail report gives you at least one row of information in your report for selected observations (patients/events) in the data set.

» Reporting severe adverse events in RCT

» DATA Steps, PROC TRANSPOSE, PROC SQL

» PROC PRINT, PROC REPORT

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Summarizing Data

Listings

Enhancements

» Labels of variables (DATA steps, PROC SQL)

» Formats for values of variables (PROC FORMAT)

» Styles and templates

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Summarizing Data

Listings

» Suicidal patients – PROC PRINT (WHERE clause)

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Summarizing Data

Listings

PROC FORMAT;VALUE $ SexF "M" = "Male"

"F" = "Female";

RUN;

PROC PRINT DATA=LOC.Dumin (FIRSTOBS=1 OBS=25) NOOBS LABEL BLANKLINE=5 N;

FORMAT Gender $SexF.;WHERE Suicide = "Yes";VAR BackgroundID Gender Race2 Age_Bin Diagnosed

Suicide Mono Med_Class Substance Subst;

RUN;

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Summarizing Data

Listings

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Summarizing Data

Listings

» Suicidal patients with ≥ 2 assessments – PROC SQL

PROC SQL;CREATE TABLE Suic_2 ASSELECT DISTINCT BackgroundID, Gender, Race2, Age_Bin,

Diagnosed, Suicide, Mono, Med_Class, Substance, Subst, Suicide

FROM LOC.DuminWHERE BackgroundID IN

(SELECT DISTINCT BackgroundID FROM LOC.Visits GROUP BY BackgroundID HAVING COUNT(ENum) > 2 )

;QUIT;

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Summarizing Data

Listings

» Suicidal patients with ≥ 2 assessments – PROC SQL

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Summarizing Data

Listings

» Safety analysis in RCTs

ID/Grp

First dose date/Last dose

date

VisitNo.

Onset date/Resolution

dateSystem Organ Class

Preferred

Term

Investigator

Term

Relationto study

drug

Trt givento AE

1356 / A 02JUL2011 / 12AUG2011 V2 01JUL2011 /

01JUL2011 Nervous system disorders Dizziness GIDDINESS FOR 3 HOURS Unknown No

1356 / A02JUL2011 / 12AUG2011 V2 01JUL2011 /

05JUL2011 Psychiatric disorders Somnolence DAY-TIME DROWSINESS Unknown No

1356 / A02JUL2011 / 12AUG2011 V6 30JUL2011 /

03AUG2011 Psychiatric disorders Somnolence DAY-TIME DROWSINESS Likely No

1463 / B 23SEP2011 / 16NOV2011 V3 07OCT2011 /

07OCT2011

General disorders and administration site conditions

Feeling hot WARM SENSATION Not related No

1463 / B23SEP2011 / 16NOV2011 V4 19OCT2011 / . Musculoskeletal and

connective tissue disorders Arthralgia LEFT ANKLE PAIN Not related No

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Summarizing Data

Summary report

» A summary report generally does some form of collapsing of observations so that each row in the report represents information from more than one observation

» Demographic and baseline clinical characteristics

» Efficacy and safety outcomes

» PROC SQL, MEANS, SUMMARY, FREQ, UNIVARIATE

» PROC TABULATE, REPORT, TRANSPOSE

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Basic Summary

Outcomes – PROC MEANS

PROC MEANS DATA=LOC.Dumin MISSING N NMISS MEAN STD MIN MAXNONOBS MAXDEC=3;

CLASS Suicide;VAR CGI_Entry CGI_1 CGI_2;

RUN;

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Basic Summary

Demographics – PROC FREQ

PROC FREQ DATA=LOC.Dumin; TABLE Gender Age_Bin

Race2 Diagnosed ;RUN;

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Basic Summary


PROC FREQ DATA=LOC.Dumin; TABLE Gender Age_Bin Race2 Diagnosed / MISSING ;RUN;

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Basic Summary


PROC FREQ DATA=LOC.Dumin ORDER=INTERNAL; TABLE CGI_1 / MISSING OUT=CGI OUTCUM;

TABLE Suicide*CGI_1 / MISSING OUT=CGIS OUTPCT;

TABLE Diagnosed*CGI_2 / MISSING OUT=CGIS OUTPCT;

RUN;

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Summary Tables

PROC TABULATE

» Very useful descriptive statistics

» Simple but powerful methods to create tabular reports

» Establishing hierarchical relationships between the variables

» Mechanisms for labeling and formatting variables and procedure- generated statistics.

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Summary Tables

Tables of summary statistics - TABULATE

PROC TABULATE DATA=LOC.Dumin ORDER=FREQ;

CLASS Gender Age_Bin Subst Race2 Med_Class Mono

;VAR CGI_1;TABLE ALL="All Patients"

Gender Age_Bin Med_Class*Mono Subst Race2, ALL="All Patients"*(N REPPCTN="%") CGI_1*(MEAN STD LCLM UCLM);

RUN;

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Summary Tables

Continuous outcome - TABULATE

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Summary Tables


DATA LOC.Dumin;SET LOC.Dumin;Sex = "Gender";Races = "Race";AgeGrp = "Age Groups";SuicP = "Suicidality";

RUN;

PROC TABULATE DATA=LOC.Dumin MISSING ORDER=FREQ;FORMAT Suicide $Sui.;

CLASS Gender Age_Bin Race2 Suicide Sex Races AgeGrp SuicP ;

VAR CGI_1;TABLE ALL="All Patients"

Sex*Gender AgeGrp*Age_Bin Races*Race2 SuicP*Suicide , ALL="All Patients"*(N REPPCTN="%")

CGI_1*(MEAN STD LCLM UCLM) /RTS=25 INDENT=4 ;RUN;

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Summary Tables


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Summary Tables

Binary outcome - TABULATE

PROC FORMAT;VALUE CGIIB 1 - 3 = "Improvement"

4 - 7 = "No Improvement"Other = " "

;RUN;

PROC TABULATE DATA=LOC.Dumin MISSING ORDER=FREQ;FORMAT CGI_1 CGIIB.;CLASS CGI_1 Gender Race2 Age_Bin Subst

Sex Races AgeGrp ASubs;TABLE ALL="All Patients"

Sex*Gender AgeGrp*Age_Bin Races*Race2 ASubs*Subst, ALL="All Patients"*(N REPPCTN="%") CGI_1*(N REPPCTN="%") /RTS=25 INDENT=4 ;

RUN;

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Summary Tables

Cell Percentages - TABULATE

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Summary Tables

Row Percentages - TABULATE

» Useful for comparison

PROC TABULATE DATA=LOC.Dumin MISSING ORDER=FREQ;FORMAT CGI_1 CGIIB.;CLASS CGI_1 Gender Race2 Age_Bin Subst

Sex Races AgeGrp ASubs;TABLE ALL="All Patients"

Sex*Gender AgeGrp*Age_Bin Races*Race2 ASubs*Subst, ALL="All Patients"*(N REPPCTN="%") CGI_1*(N ROWPCTN="%") /RTS=25 INDENT=4 ;

RUN;

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Summary Tables

Row Percentages - TABULATE

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Summary Tables

Proportions of Covariates - TABULATE

Direct cost

What are the respective contributions of the components

of direct cost and the type of cases to the total direct cost?

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Summary Tables


PROC TABULATE DATA=LOC.H_Expend FORMAT=8.2; WHERE Cost = "Direct"; CLASS CT Cost CC; VAR Value; TABLE CC* (Sum='Cost per Case' PCTSUM<CC>='% by Direct Cost' PCTSUM<CT>='% by Case Type' PCTSUM='Overall %') , Value*CT /RTS=35 INDENT=4;RUN;

PROC TRANSPOSE

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Summary Tables


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Saving Outputs

ODS RTF (Rich text format)

» Editable reports (manuscripts)

ODS PDF

» Non-editable reports

» Validation results, Safety and protocol deviations

ODS HTML

» Websites

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Saving Outputs

BasicsX “CD C:\SAS HealthCare Workshop\Figures”;

ODS RTF File=“File Name.rtf";

SAS PROCEDURES

ODS RTF CLOSE;

OPTIONS ORIENTATION=LANDSCAPEODS RTF File=“File Name.rtf“ STYLE=myStyle; TITLE ; TITLE2 ; ODS TEXT=“Include Text”;

SAS PROCEDURES

ODS RTF CLOSE;

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Outputs for Reports

Stored RFT Files (Templates)

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Outputs for Reports

Style = Journal

+ FORMAT

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Summary Reports

Employing multiple procedures, styles and format

» PROC MEANS, UNIVARIATE, FREQ, …

» PROC TABULATE, PROC REPORT

» ODS RTF

» OPTIONS Statement

» MACROS

» …

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Automated Summary Reports

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Automated Summary Reports


Some useful SAS Features

Depicting Data

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Depicting Data

SAS GRAPH - Procedures

» SGPLOT, SGPANEL, SGSCATTER, …

» GREPLAY

ODS Graphics (over 60 procedures)

» SAS/Base

» SAS/STAT, SAS/ETS, SAS/QC

Graphical representations of data are often more

easily understood and remembered than tabular

forms

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Depicting Data

ODS Graphics ONProcedure optionsDefault plot createdPLOTS=syntax [non-default plots]

ODS Graphics OFF

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Depicting Results

Regression diagnostics - Example

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Depicting Data

Basic things to note for ODS Graphics

Formats (IMAGEFMT=)

» PNG, PS, TIFF

» JPEG, GIF, PDF, WMF

X "CD C:\SAS HealthCare Workshop\Figures";

ODS GRAPHICS ON / IMAGENAME="MyFigure" IMAGEFMT=;

GRAPH/STAT PROCEDURE DATA= ;PROCEDURE OPTIONS

RUN;

ODS GRAPHICS OFF;

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Depicting Data - DUMIN

Patient selection

18475 patients

1722 (9%) Eligible patients

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Primary outcome (CGI-I)

» INSET and FORMAT statements

Requested plotFinal plot

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SGPANEL

PROC FREQ + SGPANEL

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Depicting Data

Multiple graphs in a figure

» PROC GREPLAY

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CGI outcomes

» IMAGEFMT=PS and LaTeX


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Absolute and cumulative percentages

Depicting Data

FREQ+

SGPLOT

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Remarks

Reporting results of clinical studies is mandatory

Results need to be represented concisely and precisely

SAS provides extensive tools for summarizing data and preparing results for reporting, especially when used in conjunction with other software

» Use the SAS thoughtfully

Having the right tools is important!

128



Any Questions?


Application of PROC Frequency in Clinical AnalysisGabriel Jiang Senior Statistician-Computation, Lilly-NUS Centre for Clinical Pharmacology Pte. Ltd.


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PROC FREQ

What else can we do other than counting observations and putting them in cross tabulation or n x n ways?

Common syntaxPROC FREQ <options> ;

BY variables ;

EXACT statistic-options </ computation-options> ;

OUTPUT <OUT=SAS-data-set> options ;

TABLES requests </ options> ;

TEST options ;

WEIGHT variable </ option> ;

run;

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PROC FREQ

Agenda

- Filling in zeroes with Sparse Option

- Complement with ODS Graphics

- Controlling displayed output

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PROC FREQ (1)

Scenario: I need to have lots of ‘0’ values in my tables, to show that there were no event occurrences

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ExampleLots of zeroes

PROC FREQ (1)

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PROC FREQ

Solution?

You can create zeroes in a dummy dataset, and proceed to merge with the original ‘incomplete’ dataset

Another question:

It’s troublesome to create the dummy dataset, I don’t even know which combinations are missing

Michael F. Luther

"You could create..."

135



PROC FREQ

Solution:

If you are using PROC FREQ, then there is an easy solution

Use the SPARSE Option!

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PROC FREQ

Sample code

Original code:

proc freq data=heart1;

table sex*chol_status*weight_status/ out=all noprint;

run;

New addition:

proc freq data=heart1;

table sex*chol_status*weight_status/ out=all sparse noprint;

run;

#Using data from SASHELP.HEART

137



PROC FREQ

Observations with missing Cholesterol Status

Previously missing combinations of Sex, Cholesterol Status and

Weight Status

138



PROC FREQ

Missing zero

Combinations incomplete

Output without the Sparse option

139



PROC FREQ

Cholesterol Status is missing

Previously, combination of categories that

were not available

Final Output

140



PROC FREQ (2)

Scenario: I don’t want to have to write a whole set of GPLOT statements to get plots from PROC FREQ outputs, what should I do?

141



PROC FREQ

SAS/Graph? Maybe by using PROC GPLOT?

PROC GPLOT <DATA=input-data-set> <ANNOTATE=Annotate-data-set> <GOUT=<libref.>output-catalog> <IMAGEMAP=output-data-set > <UNIFORM>;

BUBBLE plot-request(s) </option(s)>;

BUBBLE2 plot-request(s) </option(s)>;

PLOT plot-request(s) </option(s)>;

PLOT2 plot-request(s) </option(s)>;

142



Combining with ODS Graphics

ODS GRAPHICS ON;

PROC FREQ <options> ;

BY variables ;

EXACT statistic-options </ computation-options> ;

OUTPUT <OUT=SAS-data-set> options ;

TABLES requests / PLOTS < ( global-plot-options ) > < = plot-request < ( plot-options ) > > ;

TEST options ;

WEIGHT variable </ option> ;

run;

ODS GRAPHICS OFF;

143



Combining with ODS Graphics

Plots

144



PROC FREQ

Sample Code

ods graphics on;

proc freq data=heart;

table sex*chol_status*weight_status/ plots(only)=freqplot;

run;

ods graphics off;

145



PROC FREQ

Sample Output

table sex*chol_status*weight_status/ plots(only)=freqplot(orient=vertical);

146



PROC FREQ

Sample Output

table sex*chol_status*weight_status/ plots(only)=freqplot(orient=horizontal);

147



table sex*chol_status*weight_status/ plots(only)=freqplot(orient=horizontal scale=freq|log|percent|sqrt type=barchart);

148



PROC FREQ

table sex*chol_status*weight_status/ plots(only)=freqplot(orient=vertical scale=freq type=dotplot);

149



PROC FREQ

table sex/ plots(only)=cumfreqplot(orient=horizontal scale=freq);

table sex/ chisq plots(only)=deviationplot(orient=horizontal);

Cumulative Frequency Plot Deviation Plot

150



Frequency plots for multi-way tables

plots(only)=freqplot(orient=horizontal twoway=groupvertical); plots(only)=freqplot(orient=horizontal twoway=grouphorizontal);

plots(only)=freqplot(orient=horizontal twoway=stacked type=dotplot);plots(only)=freqplot(orient=horizontal twoway=stacked type=barchart);

151



Kappa plots

plots(only)=kappaplot (npanelpos=0);c plots(only)=kappaplot (npanelpos=0 stats);

plots(only)=kappaplot (npanelpos=1);

152



Kappa plots

plots(only)=kappaplot(npanelpos=0 order=descending stats); plots(only)=kappaplot(npanelpos=0 order=ascending stats);

plots(only)=kappaplot(npanelpos=0 order=ascending stats range=clip); plots(only)=kappaplot(npanelpos=0 order=ascending stats range=(-0.1,0.0));

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PROC FREQ

Kappa plot Weighted Kappa plot

plots(only)=kappaplot(npanelpos=0); plots(only)=wtkappaplot(npanelpos=0);

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Odds Ratio Plot

plots(only)=oddsratioplot(npanelpos=0 stats); plots(only)=oddsratioplot(npanelpos=0 logbase=10 stats);

plots(only)=oddsratioplot(npanelpos=0 logbase=2 stats); plots(only)=oddsratioplot(npanelpos=0 logbase=e stats);

155



PROC FREQ (3)

Scenario: I have generated some outputs using PROC FREQ… how can I change the format of the values?

156



PROC FREQ

Using the FORMAT= option

Note: The FORMAT= option applies only to cross-tabulation tables displayed in the default format. It does not apply to cross-tabulation tables produced with the LIST or CROSSLIST option.

For one way tables, you have to use PROC TEMPLATE

157



PROC FREQ

Default (BEST6./BEST7.)


table sex*chol_status*weight_status / nopercent norow nocol ;

run;

158



PROC FREQ

Using Format= option


table sex*chol_status*weight_status / nopercent norow nocol format=12.3;

run;

159



PROC FREQ

Question: If I want the layout as a list?

160



PROC FREQ

Using LIST option


table sex*chol_status*weight_status / nopercent norow nocol list;

run;

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References

Base SAS® 9.2 Procedures Guide Statistical Procedures 3rd Edition

http://bit.ly/bcT6Yt

SAS 9.2 Online Documentation

http://support.sas.com/documentation/cdl_main/index.html

Chris Moriak (2002) , The Sparse Option in PROC FREQ: Little Known, Yet Powerful, NESUG15

Mike Rhoads (2009), Changing the Numeric Format of PROC FREQ Output

http://support.sas.com/kb/24/822.html

163



Any Questions?

164



Thank you for your participation in SAS Healthcare Workshop

2011!

Have A Great Day!

If you have any further questions, you may email to

Valeria Tan, Account Manager for Healthcare Sector at [email protected]

Customer Success Team at [email protected]

mailto:[email protected]

mailto:[email protected]

sas healthcare workshop 18 nov 2011

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