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Using Adaptive Designs to Avoid Selecting the Wrong Arms in Multiarm Comparative Effectiveness Trials.

Byron J Gajewski1, Jeffrey Statland2, Richard Barohn2

  • 1Department of Biostatistics & Data Science, University of Kansas Medical Center, Mail Stop 1026, 3901 Rainbow Blvd., Kansas City, KS 66160, USA.

Statistics in Biopharmaceutical Research
|December 17, 2019
PubMed
Summary
This summary is machine-generated.

Bayesian adaptive designs, using response adaptive randomization (RAR), can efficiently select the best treatments for comparative effectiveness studies. This method avoids inadvertently excluding the most beneficial treatment, even with limited resources.

Keywords:
Bayesian methodsResponse adaptive randomizationadaptive designsclinical trialsequal randomization

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Neurology

Background:

  • Comparative effectiveness studies face resource limitations, often leading to reduced sample sizes.
  • Selecting a limited number of treatments beforehand risks excluding the most effective option.
  • Bayesian adaptive designs offer a potential solution to optimize treatment selection in clinical trials.

Purpose of the Study:

  • To demonstrate how Bayesian adaptive designs, specifically response adaptive randomization (RAR), can improve treatment selection in comparative effectiveness studies.
  • To address the challenge of 'type III errors' – inadvertently omitting the best treatment.
  • To evaluate the efficiency and power of RAR compared to traditional designs.

Main Methods:

  • Utilizing Bayesian adaptive designs with response adaptive randomization (RAR).
  • Comparing RAR with adaptive equal randomization (ER), arm(s) dropping, and fixed designs.
  • Applying the methodology to a comparative effectiveness trial for sialorrhea in amyotrophic lateral sclerosis.

Main Results:

  • Response adaptive randomization (RAR) can avoid 'type III errors' by allowing more treatments into the trial.
  • RAR demonstrates minimal loss in statistical power compared to a standard two-arm design, even when the two best arms are correctly pre-selected.
  • Significant power gains are observed when treatments are prescreened randomly before the trial.

Conclusions:

  • Bayesian adaptive designs, particularly RAR, offer an efficient strategy for comparative effectiveness research under resource constraints.
  • RAR mitigates the risk of excluding the most effective treatment, a common issue in pre-selected treatment designs.
  • This adaptive approach enhances the reliability of treatment selection and improves study power.