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
  • 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:

    Earlier podcast episode:
    Network meta-analyses: why, what, and how

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

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