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Reliably picking the best endpoint.

Dean Follmann1

  • 1Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD.

Statistics in Medicine
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Summary
This summary is machine-generated.

This study introduces a sample size ratio estimator for clinical trials with continuous endpoints. The method aids in determining the necessary sample sizes for future studies, enhancing trial design efficiency.

Keywords:
bootstrapclinical trialsprimary endpointprofile likelihoodratio of normals

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Inference

Background:

  • Endpoint selection is crucial for clinical trial design.
  • Accurate sample size estimation is vital for trial power and resource allocation.
  • Existing methods may not fully address the complexities of sample size determination for future trials.

Purpose of the Study:

  • To define and develop an estimator for the sample size ratio (θ) in clinical trials.
  • To provide a method for assessing the ratio of sample sizes needed to power future clinical trials.
  • To evaluate statistical approaches for constructing confidence intervals for the sample size ratio.

Main Methods:

  • Developed an estimator for the sample size ratio based on standardized treatment effects.
  • Utilized Welch's t-statistic for analyzing continuous endpoints.
  • Evaluated bootstrap and profile likelihood methods for confidence interval construction.
  • Applied methods to antibody abundance data from an Ebola vaccine trial.

Main Results:

  • The proposed estimator's performance was evaluated.
  • Confidence intervals were constructed using bootstrap and profile likelihood techniques.
  • The methods were successfully applied to real-world clinical trial data.

Conclusions:

  • The developed sample size ratio estimator offers a valuable tool for clinical trial planning.
  • The study provides practical methods for sample size estimation with continuous endpoints.
  • Findings contribute to more efficient and reliable clinical trial design and analysis.