Advanced multiplicity adjustment methods in clinical trials.

Area of Science:

• Biostatistics
• Clinical Trial Design
• Statistical Methodology

Background:

• Multiplicity is a common challenge in clinical trials, potentially inflating Type I error rates.
• Traditional methods for controlling familywise error rate (FWER) can be conservative, limiting statistical power.
• Recent advancements offer more flexible approaches to manage multiplicity in complex trial designs.

Purpose of the Study:

• To review and compare novel multiple testing strategies for clinical trials.
• To provide guidance for practicing statisticians in selecting appropriate methods.
• To illustrate the application of these methods with practical examples and algorithms.

Main Methods:

• Review of four related multiple testing methodologies: hierarchical testing with local significance level recycling, adaptive significance level adjustment, hierarchical family grouping, and graphical methods.
• Discussion of connections and contrasts between the methodologies.
• Provision of algorithms for calculating critical values and adjusted p-values.

Main Results:

• The reviewed methods offer enhanced flexibility in clinical trial objectives while maintaining FWER control.
• Connections between the four methodologies are elucidated.
• Practical guidance and computational tools are provided for implementation.

Conclusions:

• The reviewed multiple testing strategies provide powerful and flexible tools for clinical trial analysis.
• Understanding the relationships between these methods aids in their appropriate selection and application.
• The tutorial facilitates the practical use of advanced statistical techniques in clinical research.