SAS healthcare workshop 18 nov 2011

SAS Healthcare
Workshop 2011
18 November 2011

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

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
2

SAS Healthcare Workshop 2011

Statistical Analysis in
Healthcare Industry
Giuseppe Manai, SAS Senior Pre-sales Consultant

Jun Yao, SAS Pre-sales Consultant


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

4


5


How do we do it?

6


How do we do it?

BUSINESS SOLUTIONS

7


How do we do it?

INFORMATION MANAGEMENT

8


How do we do it?

ANALYTICS

9


How do we do it?

REPORTING

10


Healthcare Providers Using SAS

Business Issues
Facing 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.

Solution
Integration of the clinical and medical management
data analysis.

Results
Patient response times are much quicker and
effective.

11


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.

Solution
Integrates 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

12


Introduction to SAS Enterprise Guide

13


Introduction to Enterprise Guide

Quick access and manipulation of data

Carry out basic and complex analysis

Easy to use basic reporting

14


 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
15


Point-and-click interface for managing data and generating
reports.

16



17


Programming Interface

18


SAS Enterprise Guide Interface
A PROJECT is a single file that serves as a collection of:
 Data sources
 SAS programs and logs
 Tasks and Queries
 Results
 Informational Notes for Documentation

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

19


SAS Enterprise Guide Interface

20


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

21


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

22


Examples

23


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
24


Common Data Formats
 SAS datasets

 Microsoft Excel and Access

 DBMS tables

 Text files

 JMP

 HTML

 SPSS

25


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

26


Types of Graphs

The Graph menu
includes all of the
advanced graph tasks
as well as the wizards.

27


Graphs Using SAS Enterprise Guide

28


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.)

29


Summary Statistics Report
 Calculate summary statistics based on groups within the data.
 Can produce reports, graphs and data sets as output.

30


Automate a Process

To automate a process flow you simply name it

Autoexec
This will run the process flow when opening the project

31


Hands-on activity (10-15 min)

Try to:
 Import a dataset
 Create a task
 Graph the results
 Automate the process

32


Simple Linear Regression Analysis

33


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

34


Simple Linear Regression Model

b1 units

1 unit

b0

35


Simple Linear Regression Model

36


Performing Simple
Linear Regression

 Example ……

37


t-Test and ANOVA

38


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.

39


Scenario 1

 Dataset
To 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.

 Objective
Determine 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.

40


Performing Simple
Linear Regression

 Example ……

41


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.

42


Scenario 2

 Dataset
Consider 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.

 Objective
Determine 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.

43


One-Way ANOVA

 Example…….

44


Hands-on activity (10-15 min)

Explore Enterprise Guide’s tasks

 Use data sets provided

 Modify variables

 Use different tasks

 Create graphs and reports
45


Task Scheduling via Enterprise Guide

46


What’s New in SAS
Enterprise Guide 4.3


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

48


Enhancements for SAS Programmers

49


Enhanced Program Editor

Mouse over Tooltip with details for
SAS PROCS, SAS Functions

50


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

51



Converts code into Indented Format

52


Enhanced Program Editor – Analyze Program

Coverts lines of Code into Process Flow for visual representation and ease
understanding of the program flow
53


Adhoc Analysis Enhancements

54


OLAP Analyzer – Enhanced Filtering options

Enhanced filter options with
Example support

55


OLAP Analyzer - Enhanced MDX Editor

• Drag-n-Drop support to add measures/dimensions
to the MDX
• Show Line numbers for MDX

56


OLAP Analyzer - Enhanced MDX Editor

New options for Search

57


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)

58


OLAP Analyzer – Enhanced Explorer View

Apply Path to Siblings option for a
smoother data exploration
experience.
59


Integration with JMP
Data opened in EG 4.3

Data opened in JMP

60


Integration with Rapid Predictive Modeler

Self service analysis and report generation
using wizard driven RPM task

61


What’s New in SAS
Enterprise Guide 5.1


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
63


Enriched user experience and increased productivity

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

64



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

65



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

• Work with different parts of same
program in single view

66


Adhoc OLAP Analysis
Enriched user experience

Predefined Calculation Functions for
time-series data analysis
67


Adhoc OLAP Analysis
Enriched user experience

Refined Contextual Menu for
defining Totals and % of Totals

68


Reusable Queries
1  Define queries and save them
as Templates

2  Use existing queries as Sub-
Queries to filter data

69


Options for Code Submissions

Ability to define the Code
Submission options and gain
increased performance in a grid
enabled environment

70


Any Questions?

71


Tea-Break

72


SAS Healthcare Workshop

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


Outline

 Introduction

 A case study – retrospective study

 Some useful SAS features (summaries, reports)

 Remarks

74


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.

75


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
76


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

77


Introduction

 Statistical report

» Design, size, and outcomes of the study

» Patient selection flow chart
Statistical data review
» Summary statistics

» Graphs

» Analysis

 Automated summaries and report attributes
78


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
79


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

80


Some useful SAS Features
Summarizing Data


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

82


Summarizing Data

 Listings

 Enhancements

» Labels of variables (DATA steps, PROC SQL)
» Formats for values of variables (PROC FORMAT)
» Styles and templates

83


Summarizing Data

 Listings

» Suicidal patients – PROC PRINT (WHERE clause)

84


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;

85


Summarizing Data

 Listings

86


Summarizing Data

 Listings

» Suicidal patients with ≥ 2 assessments – PROC SQL

PROC SQL;
CREATE TABLE Suic_2 AS
SELECT DISTINCT BackgroundID, Gender, Race2, Age_Bin,
Diagnosed, Suicide, Mono, Med_Class, Substance,
Subst, Suicide
FROM LOC.Dumin
WHERE BackgroundID IN
(SELECT DISTINCT BackgroundID
FROM LOC.Visits
GROUP BY BackgroundID
HAVING COUNT(ENum) > 2
)
;
QUIT;
87


Summarizing Data

 Listings

» Suicidal patients with ≥ 2 assessments – PROC SQL

88


Summarizing Data

 Listings
» Safety analysis in RCTs

First dose date Onset date Preferred Investigator Relation Trt
ID Visit
/Last dose /Resolution System Organ Class to study given
/Grp No. Term Term
date date drug to AE
02JUL2011 / 01JUL2011 / GIDDINESS FOR 3
1356 / A V2 Nervous system disorders Dizziness Unknown No
12AUG2011 01JUL2011 HOURS
1356 / 02JUL2011 / 01JUL2011 / DAY-TIME
V2 Psychiatric disorders Somnolence Unknown No
A 12AUG2011 05JUL2011 DROWSINESS
1356 / 02JUL2011 / 30JUL2011 / DAY-TIME
V6 Psychiatric disorders Somnolence Likely No
A 12AUG2011 03AUG2011 DROWSINESS
General disorders and
23SEP2011 / 07OCT2011 / Not
1463 / B V3 administration site Feeling hot WARM SENSATION No
16NOV2011 07OCT2011 related
conditions
Musculoskeletal and
23SEP2011 / Not
1463 / B V4 19OCT2011 / . connective tissue Arthralgia LEFT ANKLE PAIN No
16NOV2011 related
disorders 89


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

90


Basic Summary

 Outcomes – PROC MEANS

PROC MEANS DATA=LOC.Dumin
MISSING N NMISS MEAN STD MIN MAX
NONOBS MAXDEC=3;
CLASS Suicide;
VAR CGI_Entry CGI_1 CGI_2;
RUN;

91


Basic Summary

 Demographics – PROC FREQ
PROC FREQ DATA=LOC.Dumin;
TABLE Gender Age_Bin
Race2 Diagnosed
;
RUN;

92


Basic Summary


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

93


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;

94


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.

95


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;

96


Summary Tables

 Continuous outcome - TABULATE

97


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;
Sex*Gender AgeGrp*Age_Bin Races*Race2 SuicP*Suicide
,
CGI_1*(MEAN STD LCLM UCLM) /RTS=25 INDENT=4 ;
RUN;

98


Summary Tables


99


Summary Tables

 Binary outcome - TABULATE
PROC FORMAT;
VALUE CGIIB 1 - 3 = "Improvement"
4 - 7 = "No Improvement"
Other = " "
;
RUN;

FORMAT CGI_1 CGIIB.;
CLASS CGI_1 Gender Race2 Age_Bin Subst
Sex Races AgeGrp ASubs
;
Sex*Gender AgeGrp*Age_Bin Races*Race2 ASubs*Subst
,
CGI_1*(N REPPCTN="%")
/RTS=25 INDENT=4 ;
RUN;
100


Summary Tables

 Cell Percentages - TABULATE

101


Summary Tables

 Row Percentages - TABULATE

FORMAT CGI_1 CGIIB.;
CLASS CGI_1 Gender Race2 Age_Bin Subst
Sex Races AgeGrp ASubs
;
Sex*Gender AgeGrp*Age_Bin Races*Race2 ASubs*Subst
,
CGI_1*(N ROWPCTN="%")
/RTS=25 INDENT=4 ;
RUN;

» Useful for comparison

102


Summary Tables

 Row Percentages - TABULATE

103


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?

104


Summary Tables

PROC TRANSPOSE

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;

105


Summary Tables


106


Saving Outputs

 ODS RTF (Rich text format)
» Editable reports (manuscripts)

 ODS PDF
» Non-editable reports
» Validation results, Safety and protocol deviations

 ODS HTML
» Websites

107


Saving Outputs

 Basics
X “CD C:SAS HealthCare WorkshopFigures”;

ODS RTF File=“File Name.rtf";

SAS PROCEDURES

ODS RTF CLOSE;

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

SAS PROCEDURES

ODS RTF CLOSE;

108


Outputs for Reports

 Stored RFT Files (Templates)

109


Outputs for Reports

 Style = + FORMAT
Journal

110


Summary Reports

 Employing multiple procedures, styles and format

» PROC MEANS, UNIVARIATE, FREQ, …
» PROC TABULATE, PROC REPORT
» ODS RTF
» OPTIONS Statement
» MACROS
» …

112


Automated Summary Reports

113


Automated Summary Reports

114


Some useful SAS
Features
Depicting Data


Depicting Data

Graphical representations of data are often more easily
understood and remembered than tabular forms

 SAS GRAPH - Procedures
» SGPLOT, SGPANEL, SGSCATTER, …
» GREPLAY

 ODS Graphics (over 60 procedures)
» SAS/Base
» SAS/STAT, SAS/ETS, SAS/QC

116


Depicting Data
ODS Graphics ON
Procedure options
Default plot created
PLOTS=syntax [non-default plots]
ODS Graphics OFF

117


Depicting Results
 Regression diagnostics - Example

118


Depicting Data

 Basic things to note for ODS Graphics

X "CD C:SAS HealthCare WorkshopFigures";

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

GRAPH/STAT PROCEDURE DATA= ;
PROCEDURE OPTIONS
RUN;

ODS GRAPHICS OFF;

 Formats (IMAGEFMT=)
» PNG, PS, TIFF
» JPEG, GIF, PDF, WMF
119


Depicting Data - DUMIN

 Patient selection

18475 patients

1722 (9%)
Eligible patients

120



 Primary outcome (CGI-I)
Requested plot
Final plot

» INSET and FORMAT statements
121



 Primary outcome (CGI-I) PROC FREQ + SGPANEL

SGPANEL

122



 Primary outcome (CGI-I)

123


Depicting Data

 Multiple graphs in a figure

» PROC GREPLAY

124



 CGI outcomes

» IMAGEFMT=PS and LaTeX
125


Depicting Data

 Absolute and cumulative percentages
FREQ
+
SGPLOT

126


Remarks

 Reporting results of clinical studies is mandatory

 Results need to be represented concisely and precisely

Having the right tools is important!

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

» Use the SAS thoughtfully

127


Any Questions?

128



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


PROC FREQ

 What else can we do other than counting observations
and putting them in cross tabulation or n x n ways?
 Common syntax
PROC FREQ <options> ;
BY variables ;
EXACT statistic-options </ computation-options> ;
OUTPUT <OUT=SAS-data-set> options ;
TABLES requests </ options> ;
TEST options ;
WEIGHT variable </ option> ;
run;

130


PROC FREQ

 Agenda
- Filling in zeroes with Sparse Option
- Complement with ODS Graphics
- Controlling displayed output

131


PROC FREQ (1)

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

132


PROC FREQ (1)

Example Lots of zeroes

133


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

134


PROC FREQ

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

Use the SPARSE Option!

135


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
136


PROC FREQ Previously missing combinations
of Sex, Cholesterol Status and
Weight Status

Observations with missing
Cholesterol Status

137


PROC FREQ
Output without the Sparse option

Missing zero

Combinations
incomplete

138


PROC FREQ
Final Output

Cholesterol Status
is missing

Previously,
combination of
categories that
were not available

139


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?

140


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)>;

141


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;

142


Combining with ODS Graphics
 Plots

143


PROC FREQ

Sample Code
ods graphics on;
proc freq data=heart;
table sex*chol_status*weight_status/
plots(only)=freqplot;
run;
ods graphics off;

144


PROC FREQ
Sample Output

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

145


PROC FREQ
Sample Output

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

146


table sex*chol_status*weight_status/ plots(only)=freqplot(orient=horizontal
scale=freq|log|percent|sqrt type=barchart); 147


PROC FREQ

table sex*chol_status*weight_status/
plots(only)=freqplot(orient=vertical scale=freq type=dotplot);
148


PROC FREQ
Cumulative Frequency Plot Deviation Plot

table sex/ table sex/ chisq
plots(only)=cumfreqplo
plots(only)=deviationplot(
t(orient=horizontal
scale=freq); orient=horizontal);
149


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=barchart); plots(only)=freqplot(orient=horizontal twoway=stacked type=dotplot); 150


Kappa plots

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

plots(only)=kappaplot (npanelpos=1); 151


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));
152


PROC FREQ
Kappa plot Weighted Kappa
plot

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

153


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); 154


PROC FREQ (3)

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

155


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

156


PROC FREQ
Default (BEST6./BEST7.)
table sex*chol_status*weight_status / nopercent norow nocol ;
run;

157


PROC FREQ
Using Format= option
table sex*chol_status*weight_status / nopercent norow nocol
format=12.3;
run;

158


PROC FREQ

 Question: If I want the layout as a list?

159


PROC FREQ
Using LIST option
table sex*chol_status*weight_status / nopercent norow nocol list;
run;

160


161


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

162


Any Questions?

163


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 valeria.tan@sas.com
Customer Success Team at customer.success@sas.com

164


SAS healthcare workshop 18 nov 2011

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Semelhante a SAS healthcare workshop 18 nov 2011

Semelhante a SAS healthcare workshop 18 nov 2011 (20)

Último

Último (20)

SAS healthcare workshop 18 nov 2011

Notas do Editor