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Related Experiment Videos

Adaptive design method based on sum of p-values.

Mark Chang1

  • 1Millennium Pharmaceuticals Inc., Cambridge, MA 02139, USA. mchang@mpi.com

Statistics in Medicine
|November 30, 2006
PubMed
Summary
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This study introduces a flexible adaptive clinical trial design using a linear combination of p-values. The new method allows for adaptable stopping boundaries, simplifying calculations for two-stage designs.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methods

Background:

  • Adaptive designs offer flexibility in clinical trials.
  • Existing methods like Bauer and Kohne's have limitations in stopping boundary selection.
  • Practical clinical trial needs require more adaptable stopping rules.

Purpose of the Study:

  • To propose a novel adaptive design method using a linear combination of independent p-values.
  • To enhance flexibility in selecting stopping boundaries for two-stage adaptive designs.
  • To provide a method where calculations for stopping boundaries and p-values can be performed manually.

Main Methods:

  • Utilizing a linear combination of independent p-values for hypothesis testing.
  • Developing a two-stage adaptive design without requiring numerical integration.

Related Experiment Videos

  • Investigating operating characteristics through computer simulations, with and without sample size adjustments.

    • Main Results:

    • The proposed method demonstrates significant flexibility in choosing stopping boundaries.
    • Manual calculation of stopping boundaries and p-values is feasible for two-stage designs.
    • Simulations evaluated performance across various trial types (superiority, non-inferiority) and endpoints (normal, binary, survival).

    Conclusions:

    • The novel adaptive design offers greater flexibility in stopping boundary selection compared to previous methods.
    • The method simplifies calculations, making it more practical for two-stage clinical trials.
    • Statistical efficiency is comparable to methods based on conditional power, offering a viable alternative.