Traditional multiplicity adjustment methods in clinical trials

Area of Science:

• Biostatistics
• Clinical Trials
• Statistical Inference

Background:

• Clinical trials often involve multiple objectives, treatment arms, or patient subgroups, leading to statistical challenges.
• Simultaneous assessment of several objectives can result in multiplicity, potentially compromising the integrity of statistical inferences if not properly managed.

Purpose of the Study:

• To discuss important statistical problems arising in clinical trials due to multiplicity.
• To review key concepts in multiple hypothesis testing and introduce methods for addressing multiplicity.
• To provide guidelines for developing efficient multiple testing procedures tailored to specific trial needs.

Main Methods:

• Review of fundamental concepts in multiple hypothesis testing.
• Introduction to major classes of statistical methods designed to control for multiplicity.
• Presentation of general guidelines for the development of multiple testing procedures.
• Illustration of methods using case studies with common multiplicity problems.

Main Results:

• Understanding the impact of multiplicity on statistical inferences in clinical trials.
• Familiarity with various statistical approaches to manage multiplicity.
• Guidance on selecting and developing appropriate multiple testing procedures.
• Discussion on software implementation for multiplicity adjustment methods.

Conclusions:

• Addressing multiplicity is crucial for maintaining the integrity of statistical inferences in clinical trials.
• A range of statistical methods are available to manage multiplicity, with selection based on trial-specific factors.
• The tutorial provides practical insights and case studies to aid researchers in applying these methods effectively.