Considering the unique challenges to conducting large, robust surgical trials, network meta-analyses (NMA) have become increasingly popular. Compared to conventional meta-analyses, NMAs allow for the synthesis of evidence from multiple surgical trials to compare multiple interventions and inform clinical decision-making. This book chapter will delve into the fundamentals of NMA to equip readers with the tools necessary to evaluate the application of NMA results to their clinical practice. Through an example clinical scenario and existing NMA study, the chapter will comprehensively explore methodological considerations to assess the quality of NMAs, emphasize the importance of study design, discuss various statistical frameworks and models, review interpretation of results, and evaluate the certainty of evidence using the GRADE framework modified for NMAs. This will serve as a practical guide to critically appraising and interpreting findings from surgical NMAs for clinicians.

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Network Meta-analyses in Surgical Trials

  • Lily J. Park,
  • Kevin Um,
  • Dena Zera,
  • Emmanuelle Duceppe,
  • Pablo E. Serrano

摘要

Considering the unique challenges to conducting large, robust surgical trials, network meta-analyses (NMA) have become increasingly popular. Compared to conventional meta-analyses, NMAs allow for the synthesis of evidence from multiple surgical trials to compare multiple interventions and inform clinical decision-making. This book chapter will delve into the fundamentals of NMA to equip readers with the tools necessary to evaluate the application of NMA results to their clinical practice. Through an example clinical scenario and existing NMA study, the chapter will comprehensively explore methodological considerations to assess the quality of NMAs, emphasize the importance of study design, discuss various statistical frameworks and models, review interpretation of results, and evaluate the certainty of evidence using the GRADE framework modified for NMAs. This will serve as a practical guide to critically appraising and interpreting findings from surgical NMAs for clinicians.