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You are here: Home / Innovation / Learn about different indirect comparison approaches and methods

Learn about different indirect comparison approaches and methods

By Reine Escalona on 2020-01-07 4

Learn about different indirect comparison approaches and methods
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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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Reader Interactions

Comments

  1. Markus says

    2020-03-04 at 8:26

    Hi Alex,

    regarding the rule of thumb of needing 4 times as large a sample size for an indirect comparison to obtain the same precision as a head-to-head comparison, you might have read that here (towards the end of section 2.1): https://journals.sagepub.com/doi/10.1177/0962280207080643

    Kind regards
    Markus

    Reply
    • Alexander says

      2020-03-16 at 9:02

      Thanks Markus!!

      Reply
  2. Vanessa says

    2020-05-02 at 20:20

    Hi Alexander,

    Great episode!

    The rule of thumb can be “felt” considering that the variance of the indirect comparison is the sum of the variance of each individual trial. For example:

    • Let’s consider 2 trials, trial 1: A vs Placebo and trial 2: B vs placebo with a variance V and N patients enrolled in each (to simplify let’s assume that the 2 trials have the same sample size and variance)
    • The variance of the indirect comparison is 2*V (Bucher formula)
    • To divide by 2 the variance of the indirect comparison, you need to multiply by 2 the sample size of each trial, so that the variance in each trial would become V/2
    • That’s why to compare A vs B indirectly, you need 2*N patients in both trials; i.e 4*N patients in total; compared to N patients that you would have needed if you had compared A vs B directly

    Kind regards,
    Vanessa

    Reply
    • Alexander says

      2020-05-02 at 21:26

      Awesome easy explanation!

      Reply

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