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Invited commentary: Comparing the independent segments procedure with group sequential designs.

Daniël Lakens1

  • 1Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology.

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Summary

Psychological researchers can improve efficiency using sequential analysis. This commentary critiques Miller and Ulrich's (2020) independent segments procedure, advocating for more flexible, existing sequential methods.

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Area of Science:

  • Psychological Science
  • Statistics
  • Research Methodology

Background:

  • Sequential designs offer increased efficiency in psychological research by allowing interim data analysis.
  • Miller and Ulrich (2020) proposed an independent segments procedure for sequential analysis with Type I error control.
  • Existing sequential analysis literature offers alternative procedures that may be more advantageous.

Purpose of the Study:

  • To evaluate the independent segments procedure proposed by Miller and Ulrich (2020).
  • To compare the independent segments procedure with existing sequential analysis methods.
  • To advocate for the adoption of more flexible and informative sequential designs in psychological research.

Main Methods:

  • Commentary and critical review of statistical procedures.
  • Comparison of the independent segments procedure with established sequential analysis techniques.
  • Discussion of the limitations of fixed interim analyses.

Main Results:

  • The independent segments procedure has limitations, including inflexibility in futility stopping and equivalence testing.
  • The requirement for equally spaced looks and lack of customizable error spending functions (alpha and beta) are logistical and inferential drawbacks.
  • Modern software packages (e.g., rpact) facilitate the use of more advanced and flexible sequential designs.

Conclusions:

  • The independent segments procedure is less attractive due to its restrictive nature.
  • Psychological scientists should prioritize sequential methods offering greater flexibility in design and inference.
  • Adopting advanced sequential analysis techniques can enhance research efficiency and the robustness of findings.