Introduction

The forest plot is one of the most powerful tools in statistical analysis, especially in the field of meta-analysis, where the goal is to combine results from multiple independent studies in order to reach a more comprehensive and reliable conclusion. Its use began as early as the 1970s; however, the term “forest plot” was officially established in 2001 and has since become widely adopted. The name is not accidental, as it comes from the image of a forest formed when the many lines representing the confidence intervals of studies are placed on the same horizontal axis. According to the definition by the Cochrane Collaboration, a forest plot is a graphical representation of the results of each individual study included in a meta-analysis, along with the combined overall effect. Through this representation, readers can evaluate not only the magnitude and significance of the effect but also the degree of heterogeneity between studies. This visual presentation is considered extremely useful, as it provides a quick and clear overview that allows critical interpretation of data and a better understanding of the overall effectiveness of an intervention.

Heterogeneity of Studies

One of the central issues that the forest plot highlights is heterogeneity, that is, the variability of results among the studies being combined. Heterogeneity is a critical factor, as it can affect the validity and reliability of drawing a unified conclusion from a meta-analysis. Heterogeneity can be either clinical or statistical. Clinical heterogeneity refers to differences arising from the characteristics of the populations under study, the interventions applied, the outcome measures used, and even the context or environment in which the studies were conducted. The evaluation of clinical heterogeneity always carries a degree of subjectivity, since it depends on the judgment and expertise of the researchers. In contrast, statistical heterogeneity concerns numerical differences in effect estimates and can be quantified by various measures, the most common being I², which estimates the percentage of variability due to real differences between studies rather than chance. Although there is no absolute cut-off point, the higher the I² value, the greater the heterogeneity, which requires careful assessment.

Detecting Heterogeneity

Heterogeneity can be detected both visually and statistically. In a forest plot, each study is represented by a square indicating the size of the effect and a horizontal line indicating the confidence interval. The position and length of the line provide an initial indication of the consistency or divergence between the studies. When confidence intervals overlap substantially, heterogeneity is considered low, while little or no overlap suggests higher heterogeneity. Statistically, heterogeneity is tested with Cochran’s Q test, although this test has limited power when the data are sparse or when one study disproportionately dominates the analysis. For this reason, additional methods such as meta-regression and subgroup analysis are often employed to identify the factors responsible for heterogeneity. In cases where heterogeneity is particularly high, the use of random-effects models is recommended, as they account for variability across studies, together with cautious interpretation of results.

Subgroup Analysis

Subgroup analysis is one of the most common approaches to understanding the sources of heterogeneity. Studies can be grouped according to characteristics such as the type of population, patient demographics like age or gender, the nature and intensity of the intervention, and the length of follow-up. By classifying studies into such subgroups, researchers can determine whether differences in results are linked to specific factors, thus drawing more targeted conclusions. For example, a therapeutic intervention may demonstrate strong effectiveness in younger patients but not in older ones, a finding that can guide clinical decision-making more effectively.

Structure of a Forest Plot

The typical structure of a forest plot includes several columns that provide the necessary information for interpretation. The first column usually presents the identity of each study, typically the name of the first author and the year of publication. This is followed by the presentation of data from the intervention group and the control group. The next column shows the effect size estimate, such as relative risk, together with its confidence interval. Another column graphically displays the results with squares and horizontal lines, while at the bottom of the plot a diamond shape usually appears, representing the overall combined effect estimate of the meta-analysis. The final column often provides the numerical values of the effect measure and confidence intervals, thus complementing the visual presentation with precise figures. In some cases, an additional column may be included to indicate the weight of each study, that is, the degree of influence it exerts on the overall estimate.

Conclusion

The forest plot is not merely a statistical figure, but a fundamental tool that enables researchers to summarize, compare, and interpret the results of multiple studies in a simple and comprehensible way. Its value lies in its ability to combine the completeness of numerical data with the clarity of visual presentation, making critical appraisal more accessible. Forest plots reveal trends, highlight potential discrepancies, and expose levels of heterogeneity, all of which are crucial for drawing reliable and evidence-based conclusions. In medical research, where decisions often have direct implications for clinical practice and patient care, the forest plot provides an invaluable advantage. Proper understanding and application of this tool helps avoid misleading generalizations, promotes precision in decision-making, and strengthens the transparency and robustness of scientific evidence.