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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Choosing Interim Sample Sizes in Group Sequential Designs.

Sergey Tarima1, Nancy Flournoy2

  • 1Division of Biostatistics, Medical College of Wisconsin, starima@mcw.edu.

Studies in Health Technology and Informatics
|May 27, 2021
PubMed
Summary
This summary is machine-generated.

This study examines sample sizes for interim analyses in group sequential designs (GSD). A new GSD approach using ordered alternatives avoids traditional information fractions, offering better control over power properties.

Keywords:
adaptive clinical trialshypotheses testinginterim analyseslarge sample propertieslikelihood functionsstatistical distributions

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methods

Background:

  • Group sequential designs (GSD) traditionally use information fractions to determine interim sample sizes.
  • Early stopping in GSDs complicates the distribution of maximum likelihood estimators (MLEs).
  • Existing methods for sample size determination in GSDs may be invalid for non-normally distributed data.

Purpose of the Study:

  • To investigate sample size determination for interim analyses in group sequential designs.
  • To compare the interim power properties of a classical Pocock design with a novel GSD approach.
  • To evaluate a new GSD that utilizes ordered alternative hypotheses instead of information fractions.

Main Methods:

  • Investigated sample sizes for interim analyses within group sequential designs.
  • Utilized maximum likelihood estimators (MLEs) for early stopping decisions.
  • Compared a classical one-sided three-stage Pocock design with a three-stage design based on ordered alternatives.
  • Analyzed the impact of early stopping on the distribution of test statistics.

Main Results:

  • The equivalence between stage-specific information ratios and sample size ratios is invalid with early stopping, especially for non-normal data.
  • A novel GSD approach by Tarima and Flournoy bypasses information fraction calculations.
  • This new GSD allows for interim analyses based on desired power properties.

Conclusions:

  • Traditional GSD sample size calculations may be flawed due to early stopping complexities.
  • The proposed GSD using ordered alternatives offers a more flexible and potentially more powerful approach to interim analyses.
  • This method allows researchers to prescribe interim analyses based on specific power objectives.