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!
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