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Related Experiment Video

Updated: Nov 3, 2025

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
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Response adaptive designs for Phase II trials with binary endpoint based on context-dependent information measures.

Ksenia Kasianova1, Mark Kelbert1, Pavel Mozgunov2

  • 1National Research University Higher School of Economics, Moscow, Russia.

Computational Statistics & Data Analysis
|June 4, 2021
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Summary

New clinical trial designs balance statistical power and patient response rates in rare disease studies. Response-adaptive methods using information theory offer tunable parameters for optimized trial outcomes.

Keywords:
Experimental designInformation gainPhase II clinical trialSmall population trialsWeighted information

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacoeconomics

Background:

  • Rare disease clinical trials face competing objectives: maximizing statistical power and patient response.
  • Fixed randomization designs may not optimally balance these competing objectives.
  • Response-adaptive designs, like multi-arm bandit (MAB) methods, are proposed to address this challenge.

Purpose of the Study:

  • To introduce and evaluate novel response-adaptive clinical trial designs utilizing information-theoretic criteria.
  • To investigate the tunability of these designs for balancing statistical power and patient response.
  • To compare the performance of information-theoretic designs against traditional and alternative adaptive approaches.

Main Methods:

  • Development of response-adaptive designs based on weighted information criteria (Shannon, Renyi, Tsallis, Fisher entropy).
  • Analysis of asymptotic properties of information measures for arm selection criteria.
  • Comprehensive simulation studies comparing proposed designs with fixed randomization and other adaptive methods.

Main Results:

  • Information-theoretic designs allow explicit tuning of the balance between statistical power and patient response.
  • Using exact criteria over asymptotic ones, or employing Renyi/Tsallis entropies, did not yield significant gains in power or patient allocation to superior treatments.
  • Tunable parameters in proposed designs can achieve power comparable to fixed randomization while increasing the number of patients responding to treatment.

Conclusions:

  • Novel information-theoretic response-adaptive designs offer a flexible approach to rare disease clinical trials.
  • These designs provide a mechanism to explicitly balance statistical power and patient benefit.
  • The findings suggest potential for improved trial efficiency and patient outcomes in rare disease research.