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Related Concept Videos

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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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Sample size determination for Bayesian analysis of small n sequential, multiple assignment, randomized trials

Boxian Wei1, Thomas M Braun2, Roy N Tamura3

  • 1Amgen, Thousand Oaks, CA, USA.

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|September 7, 2020
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Summary

This study introduces a Bayesian sample size calculation for small n, Sequential, Multiple Assignment, Randomized Trials (snSMART) in rare diseases. The new method requires fewer patients than frequentist approaches, improving efficiency for clinical trials.

Keywords:
Clinical trialcoverage intervalmulti-arm trialrare disease

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Rare Diseases

Background:

  • Small n, Sequential, Multiple Assignment, Randomized Trials (snSMART) are adaptive designs for rare diseases.
  • Existing methods for sample size calculation in snSMART are limited.
  • Bayesian methods offer efficiency gains for estimating treatment response rates.

Purpose of the Study:

  • To propose a Bayesian sample size calculation method for three-arm snSMARTs.
  • To enable distinguishing the best treatment from the second-best.
  • To provide an efficient alternative to existing frequentist methods.

Main Methods:

  • Developed a Bayesian sample size calculation based on asymptotic approximations.
  • Validated the method using simulations to assess statistical power in small samples.
  • Compared the proposed Bayesian method with a frequentist approach using a weighted t-statistic.

Main Results:

  • The proposed Bayesian method demonstrated desired statistical power, even with small sample sizes.
  • The Bayesian approach requires significantly fewer patients compared to the frequentist method for the same study parameters.
  • The developed methods and applet provide rapid sample size calculations.

Conclusions:

  • The Bayesian sample size calculation is an efficient and effective method for snSMARTs.
  • This approach can reduce the number of patients needed in rare disease clinical trials.
  • The proposed method offers a faster and more resource-efficient alternative for trial planning.