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Interim analysis and sample size reassessment.

M Posch1, P Bauer

  • 1Department of Medical Statistics, University of Vienna, Schwarzspanierstrasse 17, A-1090 Vienna, Austria. Martin.Posch@univie.ac.at

Biometrics
|December 29, 2000
PubMed
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Adaptive two-stage designs require careful sample size reassessment. Utilizing first-stage variability and modifying early acceptance boundaries preserves statistical power, preventing underestimation issues in hypothesis testing.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Inference

Background:

  • Adaptive two-stage designs offer flexibility in clinical trials.
  • Sample size determination is critical for maintaining statistical power.
  • Underestimating initial sample size can compromise study outcomes.

Purpose of the Study:

  • To investigate sample size reassessment in adaptive two-stage designs.
  • To evaluate the impact of early stopping rules on statistical power.
  • To propose methods for preserving power when sample size is underestimated.

Main Methods:

  • Conditional power arguments using first-stage variability.
  • Fisher's product test for comparing means of two normal populations.
  • Analysis of stopping rules and early acceptance probabilities.

Related Experiment Videos

Main Results:

  • Optimal average sample size stopping rules can decrease overall power if sample size is underestimated.
  • Low early acceptance probabilities mitigate power loss.
  • Midtrial adaptation of acceptance boundaries using first-stage data preserves power.

Conclusions:

  • Sample size reassessment is crucial for adaptive designs.
  • Modified early acceptance procedures maintain study power and are minimally anticonservative.
  • This approach enhances the reliability of two-stage trial designs.