Learn about different indirect comparison approaches and methods

Only specialized statisticians discussed indirect comparisons in the past but over the years the topic developed into something, every statistician should know about.

In this episode, Benjamin and I talk about the important reasons for using indirect comparison (IC). We specifically address the following points:

• Reasons for IC
  • H2H study design
  • HTA assessment
  • Regulatory discussions to inform the benefit-risk perspective
  • Guideline development
  • Clinical decision making
  • Bucher, 
• The classical Bucher approach vs matching adjusted indirect comparisons (MAIC)
• How to incorporated meta-analyses
• Different network-meta-analyses approaches (NMA): Bayes vs Frequentist
• systematic literature reviews (SLR)
  • Data extraction sheet
  • The iterative process of analyses
• Cochrane handbook
• Tools
  • R
  • SAS
  • Cochrane tools
• Visualizations
  • Funnel plot – publication bias
  • Forest plots – heterogeneity
  • Inconsistency assessments – only if H2H also available
• Bias 
  • Different study designs
  • Different populations
  • Not exactly the same bridge comparator
  • Differing assessments
  • Different time points
  • Multiple time points
  • Pooling of doses
  • Different analyses methods
• Precision vs bias
• Pre-specified vs post-hoc
• Secondary vs primary endpoints
• Power of IC
• Publish detailed analyses
  
  Further references:
  PRISMA http://prisma-statement.org/PRISMAStatement/
  
  Earlier podcast episode:
  Network meta-analyses: why, what, and how
  

Listen to this episode and know more about Indirect Comparison now!