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Decoupling power and type I error rate considerations when incorporating historical control data using a

Kazufumi Okada1, Shiro Tanaka2, Jun Matsubayashi3

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Summary

This study introduces a novel test-then-pool method for randomized controlled trials using historical data. The new approach enhances control over type I error and power, even with data heterogeneity, improving trial efficiency.

Keywords:
clinical trialhistorical control datapowertest-then-pool methodtype I error rate

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmaceutical Research

Background:

  • Historical control data can accelerate randomized controlled trials (RCTs) if heterogeneity is minimal.
  • The conventional test-then-pool method uses a two-sided test to assess data similarity but struggles with flexible control of type I error and power.
  • This limitation arises because the two-sided test focuses on the absolute mean difference, hindering separate adjustments for heterogeneity.

Purpose of the Study:

  • To propose a novel test-then-pool method for utilizing historical control data in RCTs.
  • To enable separate and flexible control of type I error rate and statistical power in the presence of heterogeneity.
  • To introduce a significance-level selection strategy based on maximum type I error and minimum power.

Main Methods:

  • The proposed method transforms the conventional two-sided hypothesis into two one-sided hypotheses.
  • Each one-sided hypothesis is tested with distinct significance levels, allowing independent control over type I error and power.
  • A significance-level selection approach is developed, considering maximum tolerable type I error rate and minimum desired power.

Main Results:

  • The new method maintains statistical power even with heterogeneity between historical and current control data.
  • It effectively controls the type I error rate at a level not exceeding the maximum tolerable rate.
  • The approach demonstrated utility in analyzing depression trial data and hypothetical datasets.

Conclusions:

  • The proposed test-then-pool method offers a more flexible and robust approach to incorporating historical control data in RCTs.
  • It addresses the limitations of conventional methods by allowing separate control of type I error and power.
  • This advancement can lead to more efficient and reliable clinical trial designs, particularly when dealing with data heterogeneity.