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An automated platform trial framework for A/B testing.

Wenru Zhou1, Miranda Kroehl2, Maxene Meier2

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Summary

This study explores platform trial designs for A/B testing, comparing stopping rules to optimize sample size and early stopping probability. O

Keywords:
A/B testingError spending functionInterim monitoringStopping rule

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Area of Science:

  • Clinical Trial Design
  • Statistical Methodology
  • Platform Trials

Background:

  • Platform trials enable efficient A/B testing with multiple arms and interim monitoring.
  • Investigating factors influencing sample size and early stopping is crucial for trial efficiency.

Purpose of the Study:

  • To evaluate the performance of different stopping boundaries in platform trials.
  • To provide guidance on selecting optimal trial designs based on simulation results.

Main Methods:

  • Simulated twelve scenarios with varying effect sizes and arm orders.
  • Examined O'Brien Fleming (OBF) and Pocock stopping boundaries, alongside fixed sample size designs.
  • Assessed performance with 1 or 3 interim looks.

Main Results:

  • OBF stopping is recommended for powered trials with effective variants.
  • Fixed sample size design is advised for underpowered studies.
  • Pocock boundaries with futility monitoring minimize expected sample size.

Conclusions:

  • Simulation-derived flowchart offers guidance for platform trial design.
  • Results aid high-tech companies in conducting studies with statistical rigor.
  • Informed decisions on stopping rules can enhance trial efficiency and resource allocation.