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

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 R Shiny to aid researchers in planning such designs by allowing them to easily obtain trial operating characteristics, estimate statistical power and sample size, and optimize pat

SUBMITTER: Karanevich A 

PROVIDER: S-EPMC7517602 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature