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Related Experiment Videos

Construction of a continuous stopping boundary from an alpha spending function

R A Betensky1

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA. betensky@sdac.harvard.edu

Biometrics
|September 29, 1998
PubMed
Summary

Researchers demonstrate constructing continuous stopping boundaries from alpha spending functions for flexible clinical trial monitoring. This method maintains Type I error control without pre-specifying analysis times, crucial for adaptive trial designs.

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Area of Science:

  • Statistics
  • Clinical Trial Design
  • Biostatistics

Background:

  • Traditional clinical trial monitoring methods require pre-specified analysis times and boundaries.
  • Lan and DeMets (1983) proposed a flexible approach using alpha spending functions but assumed analysis times were independent of accumulating data.
  • Frequent monitoring near a boundary can violate this assumption, necessitating adjustments to the stopping rule.

Purpose of the Study:

  • To demonstrate the construction of continuous stopping boundaries from alpha spending functions.
  • To provide a method for clinical trial design that accommodates adaptive monitoring strategies.
  • To maintain overall Type I error control in clinical trials with flexible analysis schedules.

Main Methods:

  • Utilizing the cumulative distribution function of continuous-time stopping rules as alpha spending functions.

Related Experiment Videos

  • Developing a method to derive continuous stopping boundaries from specified alpha spending functions.
  • Illustrating the application using data from the Beta-Blocker Heart Attack Trial (BHAT) and AIDS Clinical Trials Group protocol 021.

    • Main Results:

    • Successfully demonstrated the construction of continuous stopping boundaries from alpha spending functions.
    • Showcased the utility of this method in adapting to flexible monitoring schedules in clinical trials.
    • Validated the approach through practical examples in real-world clinical trial data.

    Conclusions:

    • The proposed method enables the construction of continuous stopping boundaries from alpha spending functions, enhancing clinical trial design flexibility.
    • This approach is valuable for trials where adaptive monitoring, including more frequent interim analyses, is employed.
    • The methodology ensures robust Type I error control even with non-pre-specified analysis timing based on accumulating data.