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A Bayesian adaptive blinded sample size adjustment method for risk differences.

Andrew Montgomery Hartley1

  • 1Biostatistics, PPD, 929 N Front St, Wilmington, NC, 28401, USA.

Pharmaceutical Statistics
|September 26, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel blinded sample size adjustment (SSA) method for clinical trials. The new blinded SSA method improves upon existing approaches by better maximizing expected utility in superiority or non-inferiority trials.

Keywords:
Bayesian analysisadaptive designssample size adjustmentsample size re-estimation

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Statistical Inference

Background:

  • Adaptive sample size adjustment (SSA) refines trial sample size estimates using early data.
  • Blinded SSA is preferred for logistical ease and reduced bias compared to unblinded methods.
  • Existing blinded SSA methods for binary data have limitations, including minimal treatment effect information and ignoring population uncertainties.

Purpose of the Study:

  • To propose an innovative blinded sample size adjustment (SSA) method for clinical trials.
  • To address limitations of current blinded SSA methods for binary data.
  • To enhance the efficiency and reliability of sample size determination in superiority or non-inferiority trials.

Main Methods:

  • Developed a novel blinded SSA method incorporating treatment effect evidence via a mixture distribution likelihood function.
  • Designed for primary analyses involving non-inferiority or superiority tests on risk differences.
  • Compared the proposed method against an established blinded SSA method and a fixed sample size design using an expected utility function.

Main Results:

  • The proposed blinded SSA method demonstrated superior performance in maximizing expected utility compared to established methods and fixed sample size designs.
  • The method's effectiveness was validated under various assumptions.
  • An illustrative example utilizing a Bayesian hierarchical model was presented.

Conclusions:

  • The novel blinded SSA method offers a significant improvement for adaptive clinical trial designs.
  • It effectively incorporates treatment effect information while mitigating biases and logistical issues.
  • Further research into proposed methods is recommended for broader application.