Forest plots for dichotomous outcomes and ‘O–E and Variance’ outcomes illustrate, by default:

1. the raw data (corresponding to the 2´2 tables) for each study;

2. point estimates and confidence intervals for the chosen effect measure, both as blocks and lines and as text;

3. a meta-analysis for each subgroup using the chosen effect measure and chosen method (fixed or random effects), both as a diamond and as text

4. the total numbers of participants and total numbers with events in the experimental intervention and control intervention groups;

5. heterogeneity statistics (among-study variance (tau-squared, or Tau², or τ²) for random-effects meta-analyses, the chi-squared test, the I² statistic and a test for differences across subgroups if they are present and appropriate);

6. a test for overall effect (overall average effect for random-effects meta-analyses); and

7. percent weights given to each study.

Note that 3–7 are not displayed unless data are pooled. Furthermore, the test for differences across subgroups is not displayed for Mantel-Haenszel analyses. For ‘O–E and Variance’ outcomes it is also possible to enable display of the O–E and V statistics.

Forest plots for continuous outcomes illustrate, by default:

1. the raw data (means, standard deviations and sample sizes) for each arm in each study;

2. point estimates and confidence intervals for the chosen effect measure, both as blocks and lines and as text;

3. a meta-analysis for each subgroup using the chosen effect measure and chosen method (fixed or random effects), both as a diamond and as text;

4. the total numbers of participants in the experimental and control groups;

5. heterogeneity statistics (among-study variance (tau-squared) for random-effects meta-analyses, the chi-squared test, the I² statistic and a test for differences across subgroups if they are present);

6. a test for overall effect (overall average effect for random-effects meta-analyses); and

7. percent weights given to each study.

Note that 3–7 are not displayed unless the data are pooled.

Forest plots for the generic inverse variance method illustrate, by default:

1. the summary data for each study, as entered by the author (for ratio measures these will be on the natural log (‘ln’) scale);

2. point estimates and confidence intervals, both as blocks and lines and as text (for ratio measures these will be on the natural scale rather than the log scale);

3. a meta-analysis for each subgroup using the chosen method (fixed or random effects), both as a diamond and as text;

4. heterogeneity statistics (among-study variance (tau-squared) for random-effects meta-analyses, the chi-squared test, the I² statistic, and a test for differences across subgroups if they are present);

5. a test for overall effect (overall average effect for random-effects meta-analyses); and

6. percent weights given to each study.
    

Note that 3–6 are not shown unless data are pooled. It is possible additionally to enter sample sizes for experimental and control groups. These should be entered as appropriate for the design of the study. The sample sizes are not involved in the analysis, but if entered are displayed as:

7. numbers of participants in the experimental and control group for each study;

8. the total numbers of participants in the experimental and control groups.