Building a comparative evidence base using network meta-analysis:

In today’s competitive therapeutic landscape, not all treatments have been compared directly and not all questions can be answered with available data from randomised controlled trials (RCTs). Network meta-analysis (NMA), a key comparative effectiveness approach, bridges these gaps by combining evidence across studies to deliver clearer insights, more precise estimates, and actionable treatment rankings for formulary and guideline decision-making, as part of broader evidence synthesis.

This whitepaper explains the principles, methods, and best practices of NMA, including:

• When and why to use NMA versus traditional meta-analysis for stronger comparative effectiveness
• The five stages of conducting NMA within a clear network meta analysis methodology
• Frequentist vs. Bayesian frameworks
• Tools and R packages for robust evidence synthesis, including netmeta and multinma
• Related methodologies: STC, MAIC, and ML‑NMR

What you’ll learn

• How NMA strengthens the evidence base when direct RCTs are missing
• How treatment networks enhance statistical power and comparative insights
• Practical considerations for choosing analytic approaches, including Frequentist network meta analysis and Bayesian NMA
• How NMA supports formulary decisions, HTA evaluations, and clinical guidelines