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A new program to compute and evaluate continuously monitored stopping boundaries for clinical trials
1H. Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa 33612-9497, USA. lazarien@moffitt.usf.edu
Computer Methods and Programs in Biomedicine
|March 10, 2000
Summary
This study introduces improved S-Plus code for calculating stopping boundaries in clinical trials. The code enhances existing algorithms for both one-arm and two-arm trial designs, improving efficiency and user interaction.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Statistical Software
Background:
- Continuously monitored stopping boundaries are crucial for efficient clinical trial design.
- Existing algorithms, such as those by Thall, Simon, and Estey (TSE), have limitations in one-arm trials.
- Extending these designs to two-arm trials presents unique challenges.
Purpose of the Study:
- To present and evaluate new S-Plus code for calculating and assessing continuously monitored stopping boundaries.
- To correct identified issues in the original Thall, Simon, and Estey (TSE) algorithms.
- To extend the application of these stopping boundary designs to two-arm clinical trials.
Main Methods:
- Development of S-Plus code incorporating external routines for enhanced calculation efficiency.
- Implementation of algorithms for both one-arm and two-arm clinical trial settings.
- Inclusion of a numerical integration routine for beta distribution convolutions.
Main Results:
- The presented code offers corrected and extended algorithms for stopping boundaries.
- The code is designed for interactive use by statistically proficient users.
- Efficient code execution requires a C compiler and S-Plus, tested across UNIX and Windows environments.
Conclusions:
- The developed S-Plus code provides a robust and efficient tool for implementing advanced stopping boundary designs in clinical trials.
- These improvements facilitate more effective trial monitoring and decision-making in both one-arm and two-arm studies.
- Compiled code and a beta distribution convolution routine are available, supporting broader application.
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