Introduction

Meta-analysis is an advanced statistical technique used to combine results from multiple independent studies. When studies compare event rates between treatment and control groups, researchers commonly use Relative Risk (RR) as the effect measure.

Meta-Analysis Relative Risk is widely applied in clinical trials, epidemiology, public health research, pharmaceutical studies, and evidence-based medicine. It helps determine whether an intervention reduces or increases the risk of an outcome compared with a control group.

In this tutorial, we explain how to perform Meta-Analysis Relative Risk in MedCalc, interpret the software output, understand forest and funnel plots, evaluate heterogeneity, assess publication bias, and report results correctly using the output and graphs provided.

What is Relative Risk?

Relative Risk (Risk Ratio) compares the probability of an event occurring in the treatment group with the probability of the same event occurring in the control group.

The formula is:$$RR = \frac{\text{Risk in Treatment Group}}{\text{Risk in Control Group}}$$

Interpretation of Relative Risk

Relative Risk	Interpretation
RR = 1	No difference between groups
RR > 1	Increased risk in treatment group
RR < 1	Reduced risk in treatment group
RR = 0.50	Risk reduced by 50%
RR = 2.00	Risk doubled

Biomedical Example

Suppose researchers want to evaluate whether a new antihypertensive drug reduces hypertension compared with placebo.

Each study reports:

• Total patients in treatment group
• Positive outcomes in treatment group
• Total patients in control group
• Positive outcomes in control group

These studies are then combined through meta-analysis.

Example Dataset

Study	Treat_Total	Treat_Positive	Control_Total	Control_Positive
Study 1	200	30	200	60
Study 2	180	28	180	52
Study 3	250	40	250	78
Study 4	220	35	220	65
Study 5	300	48	300	92
Study 6	280	42	280	85
Study 7	240	36	240	70
Study 8	260	39	260	75

📥 Download Dataset

Download the Excel dataset used in this tutorial:

Manual Relative Risk Calculation

Using Study 1:

Treatment Risk

30 ÷ 200 = 0.15

Control Risk

60 ÷ 200 = 0.30

Relative Risk

0.15 ÷ 0.30 = 0.50

Interpretation

RR = 0.50

This means the treatment reduced the risk of hypertension by approximately 50% compared with the control group.

Step-by-Step Meta-Analysis Relative Risk in MedCalc

Step 1

Open MedCalc.

Go to:

Statistics → Meta-analysis → Relative Risk

Step 2

Select:

Studies

Study

Intervention Group

Total Cases → Treat_Total

Positive Outcomes → Treat_Positive

Control Group

Total Cases → Control_Total

Positive Outcomes → Control_Positive

Step 3

Configure the options.

Step 4

Click OK.

MedCalc generates:

• Forest Plot
• Funnel Plot
• Relative Risk Estimate
• Heterogeneity Statistics
• Publication Bias Tests

MedCalc Options Explanation

Forest Plot

Displays:

• Individual study effects
• Confidence intervals
• Overall pooled relative risk

Recommended:

✔ Enabled

Marker Size Relative to Study Weight

Determines the square size in the forest plot.

Larger studies receive larger weights.

Fixed Effect Model Weights

Assumes all studies estimate the same true effect.

Appropriate when heterogeneity is minimal.

Random Effect Model Weights

Assumes study effects vary.

Recommended for most biomedical research.

✔ Enabled

Plot Pooled Effect – Fixed Effects

Displays pooled relative risk using fixed effect assumptions.

Optional.

Plot Pooled Effect – Random Effects

Displays pooled relative risk using random effect assumptions.

✔ Recommended

Diamonds for Pooled Effects

Shows the pooled estimate as a diamond.

The width of the diamond represents the confidence interval.

✔ Recommended

Funnel Plot

Used to evaluate publication bias.

✔ Recommended

Relative Risk Results

The analysis included:

• 8 studies
• 1,930 participants in treatment groups
• 1,930 participants in control groups

Individual Study Results

Study	Relative Risk	95% CI
Study 1	0.500	0.338–0.740
Study 2	0.538	0.357–0.812
Study 3	0.513	0.366–0.719
Study 4	0.538	0.373–0.776
Study 5	0.522	0.383–0.711
Study 6	0.494	0.355–0.688
Study 7	0.514	0.359–0.737
Study 8	0.520	0.368–0.735

Overall Pooled Relative Risk

Fixed Effects Model

RR = 0.516

95% CI = 0.456–0.585

P < 0.001

Random Effects Model

RR = 0.517

95% CI = 0.456–0.585

P < 0.001

Interpretation of Pooled RR

The pooled Relative Risk is approximately 0.52.

This means:

👉 Patients receiving treatment experienced about 48% lower risk compared with the control group.

Because:

• RR < 1
• P < 0.001

The treatment effect is statistically significant.

Forest Plot Interpretation

Squares

Represent study-specific relative risks.

Horizontal Lines

Represent 95% confidence intervals.

Diamond

Represents the pooled relative risk estimate.

Interpretation

Most study estimates cluster around RR = 0.50.

The pooled diamond is centered near 0.52.

Because the confidence interval does not cross RR = 1, the overall treatment effect is statistically significant.

Test for Heterogeneity

The MedCalc output reported:

• Q = 0.1925
• DF = 7
• P = 1.0000
• I² = 0.00%

Heterogeneity Interpretation

Q Test

P = 1.0000

Since P > 0.05:

✔ No significant heterogeneity

I² Statistic

I² = 0%

Interpretation:

• 0–25% = Low heterogeneity
• 25–50% = Moderate heterogeneity
• 50–75% = High heterogeneity
• 75% = Very high heterogeneity

Result:

✔ Studies are highly consistent.

Funnel Plot Interpretation

The funnel plot assesses publication bias.

The studies appear symmetrically distributed around the pooled estimate.

This suggests minimal evidence of publication bias.

Publication Bias Results

Egger’s Test

Intercept = 0.5148

95% CI = -1.2493 to 2.2789

P = 0.5020

Begg’s Test

Kendall’s Tau = 0.2143

P = 0.4579

Interpretation of Publication Bias

Both tests are non-significant:

• Egger’s Test P > 0.05
• Begg’s Test P > 0.05

Therefore:

✔ No significant publication bias detected.

Advantages of Relative Risk Meta-Analysis

✔ Combines multiple studies

✔ Improves statistical power

✔ Produces reliable effect estimates

✔ Supports evidence-based medicine

✔ Facilitates clinical decision-making

Applications in Biomedical Research

Relative Risk Meta-Analysis is widely used for:

• Drug efficacy studies
• Vaccine effectiveness studies
• Clinical trials
• Epidemiological investigations
• Public health interventions
• Disease prevention programs

Conclusion

Meta-Analysis Relative Risk in MedCalc is a powerful technique for combining treatment and control outcomes across multiple studies. In this example, eight studies were analyzed and the pooled relative risk was approximately 0.52, indicating a substantial reduction in risk among treated participants. The forest plot demonstrated consistent effects across studies, while heterogeneity analysis showed I² = 0%, indicating excellent consistency. Publication bias assessments using Egger’s and Begg’s tests were non-significant, suggesting reliable and unbiased results. These findings highlight the importance of Relative Risk Meta-Analysis in clinical research, epidemiology, and evidence-based healthcare.