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High-Efficiency Generation of Antigen-Specific Primary Mouse Cytotoxic T Cells for Functional Testing in an Autoimmune Diabetes Model
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Adaptive dose-finding: Proof of concept with type I error control.

Frank Miller1

  • 1AstraZeneca, Statistics & Informatics, Södertälje, Sweden. Frank.Miller@astrazeneca.com

Biometrical Journal. Biometrische Zeitschrift
|October 27, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new adaptive dose-finding method for clinical trials, focusing on trend tests rather than pairwise comparisons. It ensures type I error control even with flexible dose selection in Stage 2.

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Pharmacometrics

Background:

  • Traditional dose-finding studies often use pairwise comparisons.
  • The International Council for Harmonisation (ICH) E4 guideline allows for trend tests in dose-finding.
  • Adaptive designs offer flexibility in dose selection during a trial.

Purpose of the Study:

  • To develop and evaluate an adaptive dose-finding method using trend tests.
  • To ensure type I error control in adaptive trials with flexible dose selection.
  • To compare the performance of the proposed adaptive method against traditional approaches.

Main Methods:

  • Utilized a two-stage adaptive dose-finding design.
  • Employed trend tests based on single or multiple contrasts.
  • Applied the general conditional error approach for type I error control.
  • Introduced the Adaptive Multiple Contrast Test.

Main Results:

  • The proposed Adaptive Multiple Contrast Test controls type I error under adaptive dose selection.
  • The naive test (ignoring adaptivity) controls type I error only under specific interim decision rules.
  • Simulation studies demonstrated the performance of various adaptive tests.

Conclusions:

  • Adaptive dose-finding using trend tests is a viable strategy.
  • The conditional error approach provides robust type I error control in adaptive designs.
  • The Adaptive Multiple Contrast Test offers a flexible and statistically sound approach for dose-finding studies.