1. AA-08: Kaplan-Meier Survival Plotting Macro %NEWSURV
Jeffrey Meyers
Mayo Clinic,
Rochester,
Minnesota
The pharmaceutical and clinical oncology research fields rely heavily on time-to-event endpoints to assess the efficacy of treatments. One of the best graphical displays of time-to-event analysis is the Kaplan-Meier curve, which is simple to make but difficult to customize within SAS. My office has relied upon other software such as Microsoft Excel in order to make their customized Kaplan-Meier curves. This is incredibly inefficient as it required running the analysis in one program, exporting to another program, and manually making the graph, often with the need to create text boxes on top of the graph to show statistics. If the data was updated then the process would need to be repeated almost in its entirety. This has changed with the advent of the SAS SG graphics engine and the Graph Template Language which has improved the appearance and customizability of SAS graphics. The macro NEWSURV takes advantage of the Graph Template language to list the analyzed statistics directly into a Kaplan-Meier plot programmatically, eliminating the need for backend effort. This presentation is an overview of the features of the macro NEWSURV and a walkthrough of the main points of how the macro creates the Kaplan-Meier curves.
Give introduction here:
Pharmaceutical and clinical trial research heavily dependent on time-to-event endpoints
A need to consolidate tables and figures for journal publications and posters.
Need for efficiency
Relying on other programs tedious
Graph Template Language (GTL) adds customizability
Exploring led to the ability to add statistics into plots programmatically
Repeated programming and positive feedback led to the urge to build a macro

2. Contents Macro Overview What the macro does How the macro does it
Computed Statistics
Designing the Plot
Designing the Lattice
Statistical Report Table
Conclusion
The first section of the presentation will cover a quick overview of what the output from the macro looks like. I will then lead you through the main features of the macro to give a taste of what it is capable of creating. The description of how the macro works will focus on three key areas: How the statistics are computed and saved, how the template for the graph is designed, and how the graph creates a lattice of plots. Then I will briefly touch on a separate statistical report table that can be generated from the macro outside of the plot.

3. Macro Overview (Plot)
Simple to customize. Can be customized as much as user wants
Calculated statistics
Patients-at-risk numbers
Output formats
Scalable vector graphics
Lattice

4. Macro Overview (Statistical Report)
General Statistical Report Table Example
Event/Total
Median (95% CI)†
Hazard Ratio (95% CI)‡
Survival Estimates (95% CI)†
P-value
SASHELP.BMT Data set with no Class Variable
All Patients
83/137
1.3 ( )
1 Years: ( ) 2 Years: ( )
This is the footnote for this model
SASHELP.BMT Data set with Class Variable
Group
0.0010$
ALL
24/38
1.1 (0.5-NE)
Ref
1 Years: ( ) 2 Years: ( )
AML-High Risk
34/45
0.5 ( )
1.47 ( )
1 Years: ( ) 2 Years: ( )
AML-Low Risk
25/54
6.0 (1.9-NE)
0.56 ( )
1 Years: ( ) 2 Years: ( )
†Kaplan-Meier method; ‡Cox model; $Score test; This also allows an overall footnote
Outputs into RTF, HTML or PDF
Customizable columns, text, footnotes, and titles
Automatically generated with plot

5. Sample Dataset SASHELP.BMT GROUP – categorical
Acute Lymphoblastic Leukemia (ALL)
Acute Myeloid Leukemia (AML)
High Risk and Low Risk
T – time from transplant
STATUS – survival status
0=Alive
1=Dead
Three Class levels with group variable
acute lymphocytic leukemia
acute myeloid leukemia high risk
acute myeloid leukemia low risk
Time variable
Status variable
Only available in SAS9.3 or later

6. Key Features/Parameters
Line and Axes Transformations
Plot Summary Table Options
Dataset Transformations
Only Required Variables
YTYPE,
YMIN,
YMAX & YINCREMENT
%NEWSURV(…,
CLASSORDER=3 1 2,
CLASSREF=AML-Low Risk,
CLASSVALALIGN=left,
DISPLAY=legend total event hr pval timelist,
PLOTPVAL=logrank,
TIMELIST=0.5 1,
TIMEDX=Years);
LANDMARK
CLASSREF
TIMELIST &
TIMEDX
%NEWSURV(…,
PATTERN=2 3 4,
COLOR=blue red green,
LINESIZE=3pt, SYMBOLSIZE=10pt,
YTYPE=ppt,
YMIN=0.25,YMAX=1,YINCREMENT=0.05,
XMIN=0,XMAX=7.5,XINCREMENT=0.5,
YLABEL=Proportion Alive,
XLABEL=Disease-Free Survival Time (Years));
CLASSORDER
CLASSVALALIGN
COLOR & PATTERN
CLASS & WHERE
DISPLAY
%NEWSURV(
DATA=sashelp.bmt,
TIME=T,
CENS=status,
CEN_VL=0);
%NEWSURV(…,
CLASS=group,
WHERE=group in('ALL' 'AML-High Risk'),
XDIVISOR=30.44,
LANDMARK=3.0);
PLOTPVAL
Walk through each category of options
Make sure to describe what landmark analyses are
Pause briefly on each macro call example
XDIVISOR
XMIN, XMAX, &XINCREMENT
DATA, TIME, CENS, CEN_VL

7. Patients-at-Risk Table Variations
%newsurv(…,
RISKLABELLOCATION=left,
RISKLOCATION=bottom,
RISKLIST=0 to 7.5 by 0.5,
RISKCOLOR=1,
PARHEADER=Patients-at-Risk);
Show the different variations of the patients-at-risk table
Mention label locations
Mention coloring the numbers
Mention header
Show macro call example
RISKLABELLOCATION
RISKLOCATION
RISKCOLOR
PARHEADER

8. Lattice of Plots %NEWSURV(TIME=T, CENS=status,CEN_VL=0, SREVERSE=1|0,
NMODELS=2,ROWS=1,COLUMNS=2,
ORDER=rowmajor,
TITLE=%str(A%))|%str(B%)),
FOOTNOTE=Footnote1|Footnote2,
OVTITLE=Title for entire image,
OVFOOTNOTE=Footnote for entire image,…);
TITLE
OVTITLE
SREVERSE
Walk through the examples of different options available in a lattice of plots.
Take longer on this macro call than the others as it is more complicated
FOOTNOTE
OVFOOTNOTE

9. Output Methods RTF, PDF, HTML, Listing
Various file types (PNG, JPEG, TIFF, EMF, …)
DPI settings
Scalable vector graphics (9.3 and later)
From macro vs. within ODS tags
Quick overview of different output methods available to the macro.

10. Error Checking Macro checks most parameter input
Custom error messages:
ERROR: (Model 1: DISPLAY): TOTL is not in the list of valid values
ERROR: (Model 1: DISPLAY): Possible values are LEGEND|HR|MEDIAN|TOTAL|EVENT|TIMELIST|
PVAL|TABLECOMMENTS
ERROR: 1 pre-run errors listed
ERROR: Macro NEWSURV will cease

11. Computed Statistics 1 LIFETEST procedures, up to 2 PHREG procedures
N patients, N events
Median time-to-event
Kaplan-Meier event-free rate
Cox proportional hazards ratio
Univariate and adjusted
P-value
Patients-at-risk numbers
This section will describe the statistics that are computed within the macro, where they come from, and how they are saved into datasets
Explain why there are two LIFETEST procedures
Explain that the following slides will show a data set by data set walkthrough, but they are not all unique procedure calls.

12. LIFETEST – ODS Output SURVIVALPLOT #1
ODS GRAPHICS ON;
PROC LIFETEST DATA=sashelp.bmt
PLOT=(SURVIVAL(CL));
STRATA group;
TIME t*status(0);
ODS OUTPUT SURVIVALPLOT=os1;
RUN;
This is where the values for the Kaplan-Meier curves come from

13. LIFETEST – ODS Output SURVIVALPLOT #2
ODS GRAPHICS ON;
PROC LIFETEST DATA=sashelp.bmt
PLOT=(SURVIVAL(ATRISK=1000,2000));
STRATA group;
TIME t*status(0);
ODS OUTPUT SURVIVALPLOT=splot
(KEEP=time tatrisk stratumnum atrisk
WHERE=(tatrisk ^=.));
RUN;
This is where the values for the patients-at-risk numbers come from

14. LIFETEST – OUTSURV Option w/REDUCEOUT OPTION
PROC LIFETEST DATA=sashelp.bmt OUTSURV=os2
REDUCEOUT TIMELIST= ;
STRATA group;
TIME t*status(0);
RUN;
This is where the event-free rate estimates come from
The REDUCEOUT option is the reason why there are two LIFETEST procedures

15. LIFETEST – ODS Output CENSOREDSUMMARY
PROC LIFETEST DATA=sashelp.bmt;
STRATA group;
TIME t*status(0);
ODS OUTPUT CENSOREDSUMMARY=summary;
RUN;
This is where the number of patients and number of events come from

16. LIFETEST – ODS Output QUARTILES
PROC LIFETEST DATA=sashelp.bmt;
STRATA group;
TIME t*status(0);
ODS OUTPUT QUARTILES=quartiles;
RUN;
This is where the median time-to-event estimates come from

17. LIFETEST – ODS Output HOMTESTS
PROC LIFETEST DATA=sashelp.bmt;
STRATA group / logrank wilcoxon;
TIME t*status(0);
ODS OUTPUT HOMTESTS=pval1;
RUN;
This is where the logrank and Wilcoxon p-values come from

18. PHREG #1 – ODS Output PARAMETERESTIMATES
PROC PHREG DATA=sashelp.bmt;
CLASS group;
MODEL t*status(0) = group / RISKLIMITS;
ODS OUTPUT PARAMETERESTIMATES=hazards
(keep=classval0 hazardratio hrlowercl hruppercl);
RUN;
This is where the hazard ratios and 95% CI come from
Mention that the macro can also do adjusted hazard ratios

19. PHREG #1 – ODS Output TYPE3
PROC PHREG DATA=sashelp.bmt;
CLASS group;
MODEL t*status(0) = group / TYPE3(score lr wald);
ODS OUTPUT TYPE3=pval2
(keep=effect problrchisq probscorechisq probchisq);
RUN;
This is where the likelihood-ratio and score type 3 p-values come from

20. PHREG #2 – Adjusted Models
A second PHREG is run if adjusting covariates are added to the model
Outputs statistics with same method as PHREG #1
Output datasets limited down to CLASS variable only
This is where the likelihood-ratio and score type 3 p-values come from

21. Statistics Saved as Macro Variables
SQL Procedure
SUMMARY
OS2
QUARTILES
HAZARDS
PVAL1
PVAL2
N patients/events Event-free rates Median time-to-event Hazard ratios Logrank/Wilcoxon Score/Likelihood-ratio/Wald
PROC SQL pulls macro variables with INTO statements

22. Plot Data Set Only variables used in plot statements Step plots
Censor scatterplots
Patients-at-risk block plots
Only variables that are used in the Template procedure are included in the data set.
Plot data set
Plotting Kaplan-Meier curves
Plotting censor indicators
Plotting patients-at-risk numbers
Setting up the plot window
Summary statistics table
Be ready to explain the arrows

23. Plot Data Set – Step Plot and Censor Scatterplot Columns
Variables
Cl#_#: CLASS level
T#_#: time
C#_#: Survival function estimate when censor
S#_#: Survival function estimate
#_#:
First #: CLASS level number
Second #: model number
These set of columns for each Kaplan-Meier Curve
Explain the numbering
Explain what is meant by models
Explain what is meant by class level

24. Plot Data Set – Step Plot and Censor Scatterplot Columns
This is a sample of the columns produced by the macro to make the Kaplan-Meier curves
One set of four columns for each line

25. Multiple Plots in One Window
One STEPPLOT statement
Multiple STEPPLOT statement
STEPPLOT X=t Y=survival /
GROUP=group
NAME=‘stepplot’
LINEATTRS=(THICKNESS=4pt);
DISCRETELEGEND ‘stepplot’ / …;
STEPPLOT X=t3 Y=survival3 /
LEGENDLABEL=“AML-Low Risk”
NAME=‘stepplot3’
LINEATTRS=(THICKNESS=4pt
COLOR=cx01665E
PATTERN=8);
DISCRETELEGEND ‘stepplot1’
‘stepplot2’ ‘stepplot3’/ …;
STEPPLOT X=t1 Y=survival1 /…;
STEPPLOT X=t1 Y=survival1 /
LEGENDLABEL=“ALL”
NAME=‘stepplot1’
LINEATTRS=(THICKNESS=4pt
COLOR=blue
PATTERN=1);
STEPPLOT X=t2 Y=survival2 /
LEGENDLABEL=“AML-High Risk”
NAME=‘stepplot2’
LINEATTRS=(THICKNESS=4pt
COLOR=cxB2182B
PATTERN=4);
STEPPLOT X=t2 Y=survival2 /…;
Why are each of the lines plotted individually?
How are they?
Show there is no difference in appearance between using a group option vs. plotting each line separately.
Easier to customize when dealing with the separate stepplot statements than with a group option.

26. Turn Data by Categorical Variable into Multiple Variables
DATA _plot;
MERGE os1
(WHERE=(group=“ALL”)
RENAME=(t=t1 survival=survival1)
KEEP=t1 survival1)
os1
(WHERE=(group=“AML-High Risk”)
RENAME=(t=t2 survival=survival2)
KEEP=t2 survival2)
(WHERE=(group=“AML-Low Risk”)
RENAME=(t=t3 survival=survival3)
KEEP=t3 survival3);
RUN;
Same data set merging with itself
Keeps each class level
Renames variables

27. Plot Data Set – Patients-at-risk Block Plot Variables
Time#_#: time-point values
Atrisk#_#: Number of patients-at-risk
partitle_#: Header text string
#_#:
First #: CLASS level number
Second #: model number
_#: model number
Same naming scheme
Three columns for each of the scatterplots
Explain the EVAL Function briefly

28. How Block Plots Can Make Tables1 38 20 12 11 4 1
MARKERCHARACTER option allows a column of values to become the symbols in the scatterplot 1 2 3 4 5 6
BLOCKPLOT X=time1_1 BLOCK=atrisk1_1 / …
VALUEHALIGN=START
DISPLAY=(VALUES LABEL);
BLOCKPLOT X=time1_1 BLOCK=atrisk1_1 / …
VALUEHALIGN=START
DISPLAY=(OUTLINE VALUES LABEL);
BLOCKPLOT X=time1_1 BLOCK=atrisk1_1 / …
VALUEHALIGN=LEFT
DISPLAY=(OUTLINE VALUES LABEL);

29. Plot Data Set – Patients-at-risk Block Plot Variables
Set of columns for each class level

30. Plot Layout Plot Window
Model Title
Model Footnote
Plot Window X -
Label Y L a b e l 5 10 15 20 25 30 I N E R M A G B o c k
Model Title
Model Footnote
Plot Window X -
Label Y L a b e l
Class Level 1 2 3 5 10 15 20 25 30
Model Title
Model Footnote
Plot Window
X-Label
Y-Label 5 10 15 20 25 30
Patients - at
Risk Header 50 45 30 22 6 3 60 52 37 26 18 7 4 34 20 12 9 2
Class Level 1
Patients - at
Risk Header 50 45 30 22 6 3 60 52 37 26 18 7 4 34 20 12 9 2
First image:
Explain what layout lattice is.
Explain that layout lattice opens up more text field options
Layout lattice can be used with only one cell if only to use these other spaces for text statements
Second image:
Show how the window for each plot makes use of its own lattice layout
Titles and footnotes in sidebar blocks
patients-at-risk when set to below x-axis
Image taken from SAS 9.3 Graph Template Language Reference

31. Plot Layout Inner Margin Block
This title is placed inside of a SIDEBAR block with ALIGN=TOP
This footnote is placed inside of a SIDEBAR block with ALIGN=BOTTOM
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
Time (Years)
0.1
0.2
0.3
0.4
0.6
0.7
0.8
0.9
Proportion Alive and Disease-Free
Censor
6.0 (1.9-NE) 25 54
AML-Low Risk
0.5 ( ) 34 45
AML-High Risk
1.1 (0.5-NE) 24 38
ALL
Median (95% CI)
Event
Total
Disease Group
Inner Margin Block
This title is placed inside of a SIDEBAR block with ALIGN=TOP
This footnote is placed inside of a SIDEBAR block with ALIGN=BOTTOM
Patients-at-Risk 38 26 20 14 12 11 7 4 1 45 23 17 13 10 8 6 3 2 54 47 42 36 33 29 24 19
AML-Low Risk
AML-High Risk
ALL
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
Time (Years)
0.1
0.2
0.3
0.4
0.6
0.7
0.8
0.9
Proportion Alive and Disease-Free
Censor
6.0 (1.9-NE) 25
0.5 ( ) 34
1.1 (0.5-NE)
Median (95% CI)
Event
Total
Disease Group
Show how the schematic layout lines up with an actual plot example

32. Lattice of Plots Any rectangular array Fully Customizable
Each model’s option separated by | delimiter
Number of plots is determined by number of time variables

33. Lattice Layout of Plots Process Flow
Temporary data set w/transformations
Begin template and define number of cells
Analysis procedures
Create cell axes
Create plot and output data sets
Error
Checking
Run plot statements
Run output options
Create macro variables for ENTRY statements
Explain first, then run animation
Shows the process flow of the macro when multiple models are run in one macro call
SGRENDER procedure
Delete temporary datasets
REPORT procedure

34. Plot Summary Table
Transition into the plot summary table

35. ENTRY Statements Create text Placed inside/outside plot
LAYOUT GRIDDED / ROWS=2
HALIGN=right VALIGN=top
BORDER=true;
ENTRY HALIGN=left ‘Green Text’
/ VALIGN=top TEXTATTRS=
(COLOR=green SIZE=14pt);
ENTRY HALIGN=left ‘Black Text’
(COLOR=black SIZE=14pt);
ENDLAYOUT;
Create text
Placed inside/outside plot
Anchored by combination of position terms
Left/Center/Right
Top/Center/Bottom
Powerful w/LAYOUT GRIDDED
Green Text
Black Text
Red Text
Black Text
Green Text
ENTRY HALIGN=left ‘Green Text’
/ VALIGN=top TEXTATTRS=
(COLOR=green SIZE=14pt);
ENTRY HALIGN=left ‘Black Text’
(COLOR=black SIZE=14pt);
ENTRY HALIGN=center 'Red Text‘
/ VALIGN=top TEXTATTRS=
(COLOR=red SIZE=14pt);
ENTRY HALIGN=left ‘Blue Text’
/ VALIGN=bottom TEXTATTRS=
(COLOR=blue SIZE=14pt);
ENTRY HALIGN=right "X"
{sup "2"} {unicode "2264"x}
"64" / VALIGN=bottom
TEXTATTRS=(COLOR=black
SIZE=14pt); 1 X 2
<64
Blue Text 1 2 3 4 5 6
Walk through the different entry statements
Give a description of layout gridded

36. LAYOUT GRIDDED Class N Median (95% CI) Time-Point KM Est ALL 38
1.1 (0.5-NE)
1 Year
0.55
2 Years
0.35
AML High-Risk 45
0.5 ( )
0.38
AML Low-Risk 54
6.0 (1.9-NE)
0.78
Makes a rectangular grid
Each cell can contain
ENTRY statements
Plot statements
Legend statements
Automatically sized
Can be nested within itself

37. Non-Visible Items Still Take up Space
STEPPLOT X=t1 Y=survival1 / LINEATTRS=(color=white)
NAME='white' LEGENDLABEL='White Line';
STEPPLOT X=t1 Y=survival1 / NAME=‘class1’ …;
STEPPLOT X=t2 Y=survival2 / NAME=‘class2’ …;
STEPPLOT X=t3 Y=survival3 / NAME=‘class3’ …;
LAYOUT GRIDDED / COLUMNS=1 ROWS=2 …;
DISCRETELEGEND ‘class1’ ‘class2’ ‘class3’ / …;
DISCRETELEGEND 'white' / BACKGROUNDCOLOR=red
OPAQUE=true …;
ENDLAYOUT;
250
500
750
1000
1250
1500
1750
2000
2250
2500
2750
3000
Disease-Free Survival Time
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Survival Distribution Function Estimate
White Line
AML Low-Risk
AML High-Risk
ALL
White Line
AML Low-Risk
AML High-Risk
ALL
Explain that the next two slides include tricks that are used in order to make the plot summary table
This slide contains an example of an item that cannot be seen, but can still be made useful for plotting purposes.
Focus on text by the arrow

38. Rendering in Order
250
500
750
1000
1250
1500
1750
2000
2250
2500
Disease-Free Survival Time
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Survival Distribution Function Estimate
ALL Black
ALL
AML-High Risk
AML-Low Risk
SCATTERPLOT X=t2 Y=survival2 / MARKERATTRS=(symbol=diamondfilled color=black) NAME='black' LEGENDLABEL='ALL Black';
SCATTERPLOT X=t Y=survival / MARKERATTRS=(symbol=diamondfilled) GROUP=group NAME='group';
DISCRETELEGEND 'black' 'group' / …;
Explain how choosing a specific rendering order can lead to useful features
Overwriting certain lines
Using objects for legends, creating new looking graphics, etc.

39. Plot Summary Table Design+ . 61 ( 49 - 76 )
Legend
Class Value
Total
Events
Time
Point
KM Estimate
Score P
value :
0010
Censor
Table Comment 1 2 3
ALL
AML
High Risk
Low Risk 38 45 54 24 34 25
Years 55 41 73 35 23 26 15 78 67 90
Walk through how to next layout gridded statements in order to create a formatted table
Show where the previous tricks come into play
Explain dynamic ability to change

40. Statistical Report Table
General Statistical Report Table Example
Event/Total
Median (95% CI)†
Hazard Ratio (95% CI)‡
Survival Estimates (95% CI)†
P-value
SASHELP.BMT Data set with no Class Variable
All Patients
83/137
1.3 ( )
1 Years: ( ) 2 Years: ( )
This is the footnote for this model
SASHELP.BMT Data set with Class Variable
Group
0.0010$
ALL
24/38
1.1 (0.5-NE)
Ref
1 Years: ( ) 2 Years: ( )
AML-High Risk
34/45
0.5 ( )
1.47 ( )
1 Years: ( ) 2 Years: ( )
AML-Low Risk
25/54
6.0 (1.9-NE)
0.56 ( )
1 Years: ( ) 2 Years: ( )
†Kaplan-Meier method; ‡Cox model; $Score test; This also allows an overall footnote
Walk through highlighting the customizability of the table
Imports the titles/footnotes from plots
Automatically generated footnotes

41. Multiple Macro Calls Can be Output in the same Report
Event/Total
Survival Estimates (95% CI)†
All Patients, In Years, Survival at 1 Year, Using Proportions
Disease Group
ALL
24/38
1 Year: ( )
AML-High Risk
34/45
1 Year: ( )
AML-Low Risk
25/54
1 Year: ( )
This comes from the first macro call
Time Landmarked at 500 Days, In Days, Subsequent Survival at 365 Days, Using Percentages
3/16
365 Days: ( )
2/13
365 Days: ( )
7/36
365 Days: ( )
This comes from the second macro call
†Kaplan-Meier method;
%NEWSURV(…,
summary=0,out=table);
%NEWSURV(…, summary=1,out=table,
newtable=0);
Ability to make multiple runs of the macro save to the dame dataset
Ability to make the last macro call output the dataset with all of the analysis

42. Contact Information
Name: Jeffrey Meyers
Enterprise: Mayo Clinic
Address: 200 1st St SW
City, State ZIP: Rochester, MN 55905
Work Phone:
Twitter: jmeyers_spa