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.
/* 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;