How to read and interpret the results of a Bayesian network meta-analysis: a short tutorial.

Area of Science:

• Biostatistics
• Medical Informatics
• Evidence-Based Medicine

Background:

• Pairwise meta-analyses are commonly presented using forest plots.
• Network meta-analysis (NMA) results can be complex and difficult to interpret for many readers.
• Traditional forest plots are often not included in NMA, increasing interpretability challenges.

Purpose of the Study:

• To provide a layperson-friendly tutorial on interpreting Bayesian network meta-analysis (BNMA) output.
• To clarify the understanding of novel BNMA outputs, such as treatment rankings and probability distributions.
• To serve as a guide for reading BNMA outcomes, distinct from conducting the analysis or assessing bias.

Main Methods:

• Utilizes realistic data for a Bayesian network meta-analysis.
• Focuses on interpreting the analysis output rather than the methodology or bias assessment.
• Employs a tutorial approach to explain complex statistical results.

Main Results:

• Bayesian network meta-analyses offer unique outputs like treatment rankings and probability distributions.
• These novel outputs provide deeper insights into treatment comparisons.
• Understanding these outputs is crucial for evidence-based decision-making.

Conclusions:

• Interpreting BNMA results, including treatment rankings and probability distributions, is essential for clinicians and researchers.
• This tutorial aims to demystify BNMA output interpretation.
• Enhanced understanding facilitates better application of NMA in healthcare.