📊 How to Perform a Network Meta-Analysis (NMA): Step-by-Step Guide for Beginners
Network Meta-Analysis (NMA), also known as multiple treatments meta-analysis or mixed treatment comparison, is a powerful statistical method used to compare multiple interventions simultaneously, even if they haven’t been directly compared in a single study.
If you’re a student, researcher, or health professional who wants to learn how to perform a network meta-analysis, this blog post will walk you through it in a simple and practical way.
🧠 What is Network Meta-Analysis?
Network Meta-Analysis combines direct evidence (from studies that compare two treatments head-to-head) and indirect evidence (from studies that share a common comparator) to estimate the relative effects of multiple treatments.
For example, if:
- Study A compares Drug X vs Drug Y
- Study B compares Drug Y vs Drug Z
Then NMA allows you to indirectly compare Drug X vs Drug Z, even if no study directly compared them.
✅ When to Use Network Meta-Analysis
Use NMA when:
- You want to compare 3 or more interventions.
- Not all interventions have been compared in head-to-head trials.
- You want a ranking of treatments based on effectiveness or safety.
🛠️ Step-by-Step Guide: How to Perform a Network Meta-Analysis
🔍 Step 1: Define Your Research Question
Use the PICO framework:
- Patient population
- Interventions
- Comparators
- Outcomes
Example:
“Among adults with hypertension, which of the following medications—A, B, or C—is most effective in reducing blood pressure?”
📚 Step 2: Conduct a Systematic Literature Search
Search databases like:
- PubMed
- Embase
- Cochrane Library
Include RCTs (Randomized Controlled Trials) that compare at least two of your interventions.
Use a PRISMA flow diagram to track the selection process.
📋 Step 3: Extract and Organize Data
Create a table with:
- Study ID
- Interventions compared
- Outcome measures (e.g., mean BP reduction)
- Sample size
- Standard deviations or confidence intervals
Make sure to standardize the outcome units across studies.
🧩 Step 4: Construct the Network
Draw a network diagram:
- Each node is a treatment.
- Each edge is a direct comparison.
You can use software like:
- CINeMA (Confidence In Network Meta-Analysis)
- R (netmeta, gemtc packages)
- STATA (using
networkormvmeta) - RevMan + NetMetaXL (Excel tool)
📈 Step 5: Perform the Analysis
You can do this in frequentist or Bayesian frameworks.
Frequentist (easier for beginners):
- Use the
netmetapackage in R. - Calculate effect sizes (e.g., odds ratio, risk ratio, mean difference).
- Account for heterogeneity (differences between studies).
Bayesian (more advanced):
- Use the
gemtcpackage in R or WinBUGS/JAGS for MCMC modeling. - Good for complex models and uncertainty estimation.
🧪 Step 6: Assess Inconsistency and Heterogeneity
Inconsistency occurs when direct and indirect evidence don’t agree.
Heterogeneity measures variability between studies.
Tools:
- Node-splitting analysis (for inconsistency)
- I² statistic (for heterogeneity)
- Comparison-adjusted funnel plots (for publication bias)
📊 Step 7: Rank the Interventions
Rank treatments using:
- SUCRA scores (Surface Under the Cumulative Ranking curve)
- Rankogram plots
A SUCRA score of 100% means a treatment is likely the best; 0% means it’s the worst.
📝 Step 8: Interpret and Report Results
Your final report should include:
- Network diagram
- Forest plots of treatment effects
- SUCRA rankings
- Discussion of clinical relevance
- Limitations and bias assessments
Follow PRISMA-NMA guidelines for transparent reporting.
💡 Tips for a Successful NMA
- Always check assumptions: transitivity (are the studies similar enough to compare?), consistency, and homogeneity.
- Don’t mix study designs (e.g., avoid combining RCTs and observational studies unless justified).
- Use GRADE approach to assess the quality of evidence.
📚 Example Tools for Network Meta-Analysis
| Tool | Use |
|---|---|
R (netmeta, gemtc) | Free and powerful statistical computing |
STATA (network, mvmeta) | Advanced modeling |
| RevMan + NetMetaXL | Beginner-friendly Excel plugin |
| CINeMA | Online tool for confidence rating |
| WinBUGS/JAGS | For Bayesian NMA |
🧠 Final Thoughts
Network Meta-Analysis is a valuable tool when you need to compare multiple treatments in the absence of direct evidence. Though the process may seem complex at first, following a step-by-step approach—and using the right software—makes it much more manageable.
Whether you’re conducting a thesis, writing a research paper, or comparing drug options for clinical use, NMA helps you make evidence-based decisions with broader insight.
✍️ Need Help with Your NMA Project?
Let me know in the comments or contact me directly if you’d like help with:
- Choosing the right software
- Running the analysis in R
- Visualizing your network
- Writing the NMA section for your paper