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Group sequential trials revisited: simple implementation using SAS.

John Whitehead1

  • 1Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, UK. j.whitehead@lancaster.ac.uk

Statistical Methods in Medical Research
|September 30, 2010
PubMed
Summary
This summary is machine-generated.

Group sequential trial designs offer significant benefits and are widely accepted. This article demonstrates how to easily implement various group sequential designs using two SAS functions, PROBBNRM and SEQ.

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Statistical Software Applications

Background:

  • Group sequential trial methodology is well-established and recognized for its advantages in clinical research.
  • Proper implementation of group sequential designs is accepted by both researchers and regulatory bodies.

Purpose of the Study:

  • To demonstrate the straightforward implementation of diverse group sequential trial designs using accessible SAS functions.
  • To provide researchers with practical tools for designing and analyzing studies with interim analyses.

Main Methods:

  • Utilizing two SAS functions: PROBBNRM (standard function) and SEQ (PROC IML).
  • Focusing on the essential aspects of group sequential design, including study evaluation and sample size distribution.
  • Discussing the conduct of interim analyses and the impact of deviations from the planned data accrual schedule.

Main Results:

  • Illustrative examples and SAS code listings are provided for practical application.
  • The computations for final analysis are shown to be closely related to design-stage computations.
  • The ease of implementing a wide range of group sequential designs using the specified SAS functions is highlighted.

Conclusions:

  • Group sequential trial designs can be implemented efficiently and straightforwardly using the PROBBNRM and SEQ SAS functions.
  • The provided methods facilitate the design, interim analysis, and final analysis of clinical trials with sequential monitoring.
  • Researchers can leverage these SAS tools to effectively manage and analyze group sequential trials.