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Interim analyses in clinical research.

D A Berry1

  • 1School of Statistics, University of Minnesota, Minneapolis 55455.

Cancer Investigation
|January 1, 1987
PubMed
Summary
This summary is machine-generated.

Interim analyses in clinical studies can be viewed differently by statisticians. While significance testing suggests they alter conclusions, the Bayesian approach finds no impact, allowing continuous monitoring.

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Area of Science:

  • Clinical Trials Methodology
  • Statistical Inference
  • Biostatistics

Background:

  • Interim analyses are conducted before a clinical study's scheduled completion.
  • Motivations include assessing early evidence or satisfying curiosity.
  • Statistical interpretations of interim analyses differ significantly.

Purpose of the Study:

  • To explain the differing statistical views on interim analyses.
  • To provide recommendations for designing and analyzing studies incorporating interim analyses.

Main Methods:

  • Explanation of the significance testing perspective on interim analyses.
  • Explanation of the Bayesian perspective on interim analyses.

Main Results:

Related Experiment Videos

  • Significance testing view: Interim analyses impact study inferences, even with identical data.
  • Bayesian view: Interim analyses do not penalize conclusions and allow continuous monitoring.

    • Conclusions:

    • The choice of statistical approach (significance testing vs. Bayesian) dictates the impact of interim analyses.
    • Recommendations are provided for optimal study design and analysis when employing interim analyses.