1. Forest Plotting Analysis Macro %FORESTPLOT (Paper 3419)
Jeffrey Meyers
Qian Shi, PhD.
Mayo Clinic, Rochester MN
The forest plot is a graph commonly associated with meta analysis. It allows for a quick visual comparison of estimates across subgroups or variable levels. They are not as commonly used as other graphics because of the amount of work to produce one. The estimate and any other statistics to be displayed must be calculated, added to a final plot dataset, and then plotted. If a forest plot is comparing multiple model outputs, then the code to produce the statistics becomes replicated and prone to errors. The forest plot macro fully automates multiple types of analyses including Cox proportional hazards regression, logistic regression, Kaplan-Meier event-free rates, and binomial success rates, and gives a vast amount of customization options. The macro also includes the flexibility of being able to input a pre-generated dataset to transform into a high quality forest plot.
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2. Forest Plot Example (Graphically Speaking)
The forest plot is a graph commonly associated with meta analysis. It allows for a quick visual comparison of estimates across subgroups or variable levels. They are not as commonly used as other graphics because of the amount of work to produce one. The estimate and any other statistics to be displayed must be calculated, added to a final plot dataset, and then plotted. If a forest plot is comparing multiple model outputs, then the code to produce the statistics becomes replicated and prone to errors.
Lets look at this graph from Sanjay’s Graphically Speaking blog and assume that each of these models is a single comparison. This would mean up to 9 models would have to be run, combined, and output to make this one graph. I believe this is one of the main contributors to why forest plots are not used as often as other graphics.
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Copyright © 2013, SAS Institute Inc. All rights reserved.

3. Forest Plot Example (%FORESTPLOT)
The code to actually produce a forest plot from a dataset is not complicated, especially in SAS This is an example of my macro using nearly the same dataset as the previous slide to generate a custom forest plot. The macro has some differences, namely some of the formatting. The FORESTPLOT macro can use pre-generated data to output a forest plot, but that is not its main purpose.
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4. Purpose
Creation of high quality forest plots with high level of customizations
Automate the calculations of ratios and estimates involved with forest plot
Logistic and Cox proportional hazards regression, Kaplan-Meier time-point estimates, binomial success rate, user provided data
Analysis across factors of a variable, subgroups, or in Univariate and multivariate models
Automate the creation of a dataset most conducive to producing a forest plot
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Copyright © 2013, SAS Institute Inc. All rights reserved.

5. Plot Setup Graph is designed with Graph Template Language (GTL)
Two separate versions for SAS 9.2 and 9.3+
Due to limitations within 9.2 alleviated in 9.3+
Nearly every piece of graph is customizable
Can be output in many formats (PNG, JPEG, TIFF, EMF, PDF, PS, etc.)
Size, location, and name of file customizable
Can be included within ODS tags in larger report

6. Macro Process METHOD Determines Analysis Method Cox Regression Method
Kaplan-Meier
Estimate Method
Transform Dataset
into Final
Plot Dataset
Create Template
And
Generate Plot
Error
Checking
Logistic Regression
Method
Analysis +
Output Dataset
Inputs
Binomial Success
Rate Method
Inputted Data
Method
This slide will have the “consort diagram” of the macro process flow
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Copyright © 2013, SAS Institute Inc. All rights reserved.

7. Sample Datasets (Logistic/Binomial)
Neuralgia dataset provided by SAS
Link to dataset creation
Treatment:
A and B are active arms
P stands for Placebo arm
Insert table of first several rows of dataset
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Copyright © 2013, SAS Institute Inc. All rights reserved.

8. Sample Datasets (Kaplan-Meier)
BMT dataset provided by SAS
Located in SASHELP library
(version 9.3 or later)
Version 9.2 creation
T: time from transplant to death
Status: 0=Alive, 1=Dead
Group=Disease Category
ALL=Acute Lymphoblastic Leukemia
AML=Acute Myeloid Leukemia
Insert first few rows of dataset
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9. Estimates Across Levels of a Factor
Statistics
In order requested
X-axis options
Labels, range, type
Automatic label
Automatic levels
Reference line

10. Customized Title Automatic footnotes Categorical factors
Level vs reference
Multiple formats
Continuous factors
Adjustable step size
Markers
Symbols, colors, size
Line thickness
Changeable by model
Plot walls

11. Disabled automatic footnotes Spaces between models
Headers
Super or Subscripts
Line breaks
Manual footnotes
Categorical format
2 levels condensed
Continuous format
Condensed to one row
Disabled automatic footnotes
Spaces between models

12. Column header for model titles Specific factors listed from each model
2 level factors can be combined into one row
Model level statistics
Concordance
N Events/Total
P-values
Factor level statistics
Ratios/Rates

13. Example of Subgroup Analysis
Model Titles
Where clauses
Separate by model
Line-caps disabled
Separate p-values by model
Multiple time-points
Text overflows into plot
Add examples of different kinds of forest plots with different formatting
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Copyright © 2013, SAS Institute Inc. All rights reserved.

14. Example of Subgroup Analysis
Use of multiple BY variables
Pooled value for comparison
Multiple formats available
Default label
Manual label
Add examples of different kinds of forest plots with different formatting
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Copyright © 2013, SAS Institute Inc. All rights reserved.

15. Plot Example (SAS 9.2) Scalable vector graphics unavailable
Pooled value for comparison
More tabular display
Reference on own row

16. Closing Forest plots are a versatile method for displaying data
%FORESTPLOT greatly reduces effort required to build a forest plot
Goals for the future:
More types of analyses such as linear regression
Allow more panels of plots and statistics

17. References First Example Concordance Methods:
“Graphically Speaking: Forest Plot with SAS 9.3.” Sanjay Matange, January 6, 2013
Concordance Methods:
Cox Regression: Therneau T (2014). _A Package for Survival Analysis in S_. R package version , <URL:
Logistic Regression: cNeil BJ: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29-36, 1982.

18. Contact Information
Name: Jeffrey Meyers
Enterprise: Mayo Clinic
Address: 200 1st St SW
City, State ZIP: Rochester, MN 55905
Work Phone:
Twitter: jmeyers_spa
SAS Community Wiki Page: Link
The forest plot macro greatly alleviates the amount of effort required to produce a high quality forest plot, and gives a vast amount of customizability to meet the users needs. Forest plots are very useful beyond meta-analysis and are more informative than a basic table.