Clinicians, patients, and health-policy makers often need to decide which treatment is “best” based on all relevant evidence, but randomized controlled trials (RCTs) that simultaneously compare all interventions of interest are rarely available. Network meta-analysis (NMA) is an approach which may be used to assess relative treatment differences, by combing data from more than one RCT. This method to leverage the existing published data has widely increased in use, making it more and more critical that you understand the underlying assumptions when performing and interpreting results from NMA.
Join us for this webinar where you’ll learn:
- Terms such as direct and indirect comparisons, mixed-treatment comparisons, and network meta-analyses and why they are used.
- The difference between fixed and random effects and Frequentist and Bayesian methods.
- Concepts, assumptions, and limitations of NMAs, such as heterogeneity, inconsistency, and bias.
Want more in-depth information? Attend our webinar on Advanced Network Meta-analysis: Recent Developments in Bayesian NMA