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Am Stat. 2019;2019. doi: 10.1080/00031305.2019.1610065. Epub 2019 Jun 24.

Optimizing Sample Size Allocation and Power in a Bayesian Two-Stage Drop-The-Losers Design.

The American statistician

Alex Karanevich, Richard Meier, Stefan Graw, Anna McGlothlin, Byron Gajewski

Affiliations

  1. Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS.
  2. EMB Statistical Solutions, LLC, Overland Park, KS.
  3. Berry Consultants, LLC, Austin, Texas.

PMID: 32981939 PMCID: PMC7517602 DOI: 10.1080/00031305.2019.1610065

Abstract

When a researcher desires to test several treatment arms against a control arm, a two-stage adaptive design can be more efficient than a single-stage design where patients are equally allocated to all treatment arms and the control. We see this type of approach in clinical trials as a seamless Phase II - Phase III design. These designs require more statistical support and are less straightforward to plan and analyze than a standard single-stage design. To diminish the barriers associated with a Bayesian two-stage drop-the-losers design, we built a user-friendly point-and-click graphical user interface with

Keywords: R Shiny; adaptive designs; power; seamless phase II/III clinical trial; staged design; two-stage trial

References

  1. Biom J. 2005 Jun;47(3):257-68; discussion 269-81 - PubMed
  2. Stat Methods Med Res. 2017 Feb;26(1):508-524 - PubMed
  3. Biometrics. 1989 Jun;45(2):537-47 - PubMed

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