# Handy Forest Plots for Interpreting Clinical Data Graphically

Forest plots can be handy in interpreting multiple studies data or data from subgroups in a understandable graphical representation.

**Forest plots for Meta-Analysis:**

- Forest plots show the information from the individual studies that went into the metaanalysis at a glance.
- They show the amount of variation between the studies and an estimate of the overall result.
- In case of medical research, it is a graphical display designed to illustrate the relative strength of treatment effects in multiple quantitative scientific studies addressing the same question.
- It was developed for use in medical research as a means of graphically representing a meta-analysis of the results of randomized controlled trials.

For example: A Forest plot is shown in the figure below.

- It is for a study conducted in different countries. Odds ratio with 95% CI were calculated for each region and overall using meta-analysis.
- Size for square box is showing the %weight of study for that particular region.
- Endpoints of Line through square box represent Confidence interval.
- Diamond at the end is representing the odds ratio for overall study.

**Forest plots for Subgroup Analysis:**

- Purpose of Subgroup Analysis: Determining whether or not there is heterogeneity in a treatment effect—ie, that a treatment works better in some subgroups than others—is fraught with statistical difficulties and has led to much misinterpretation.
- Subgroup analysis should concentrate on differences from the average overall treatment effect, via tests of heterogeneity or interaction, and that it is inappropriate to assess the effects of treatment on a single subgroup by examination of the 95% CI for that subgroup (as referred in Figure below).
- Confidence intervals in subgroups are always wider than those for the main effect because of smaller numbers.
- If the interval for a subgroup crosses the no effect point, this is widely misinterpreted as a lack of effect in the subgroup even when the overall effect is significant.
- The correct approach is to determine whether the effect size for different subgroups varies significantly from the main effect by a test for heterogeneity.
- Forest plots have become useful, yet in their standard presentation, they tend to encourage misinterpretation.
- Interpretation of subgroup effects would be helped if this line was de-emphasized or omitted and replaced by a bold vertical line at the overall treatment effect level, making it easier to see if a subgroup confidence interval differed significantly from the overall effect.

**Sample Code:**

```
/* Set the graphics environment */
goptions reset=all cback=white border htitle=12pt htext=10pt;
/* Create sample data for forest plot. */
data test;
input yvar $ 1-10 lower_limit rate upper_limit;
datalines;
Sohn 2002 1.2 1.5 2.2
Raine 2003 2.2 2.5 3.0
Snow 1999 0.8 1.3 4.4
; run;
/* Create an annotate data set to draw the lines. */
data anno;
length function style color $8;
retain xsys ysys '2' when 'a';
set test;
/* Draw the horizontal line from lower_limit to upper_limit */
function='move'; xsys='2'; ysys='2'; yc=yvar; x=lower_limit; color='black'; output;
function='draw'; x=upper_limit; color='black'; size=1; output;
/* Draw the tick line for the lower_limit value */
function='move';xsys='2'; ysys='2';yc=yvar; x=lower_limit; color='black'; output;
function='draw';x=lower_limit; ysys='9'; y=+1; size=1; output;
function='draw';x=lower_limit; y=-2; size=1;output;
/* Draw the tick line for the upper_limit value */
function='move';xsys='2'; ysys='2'; yc=yvar; x=upper_limit; color='black'; output;
function='draw';x=upper_limit; ysys='9'; y=+1; size=1; output;
function='draw';x=upper_limit; y=-2; size=1; output;
run;
title1 'Forest Plot with PROC GPLOT';
axis1 label=none
minor=none
offset=(5,5);
axis2 order=(0 to 4.5 by 0.5)
label=('Odds Ratio')
minor=none;
symbol1 interpol=none color=black value=dot height=1.5;
proc gplot data=test;
plot yvar*rate / annotate=anno
nolegend
vaxis=axis1
haxis=axis2
href = 1
lhref = 2;
run;
quit;
```