Clinical trials with multiple outcomes: a statistical perspective on their design, analysis, and interpretation

Area of Science:

• Clinical Trials Methodology
• Biostatistics
• Pharmaceutical Research

Background:

• Clinical trials often involve multiple outcomes, posing statistical and practical challenges.
• Inconsistent handling of multiple outcomes can lead to biased results and misinterpretation.
• Existing methods for managing multiple outcomes may not adequately address all complexities.

Purpose of the Study:

• To address practical and statistical issues in handling multiple outcomes in clinical trials.
• To provide guidance on trial design, analysis, and reporting for multiple outcomes.
• To highlight the importance of prespecified priorities and flexible interpretation.

Main Methods:

• Discussion of statistical concepts including corrections for multiple significance testing.
• Illustration of methods using examples such as global tests and combined outcomes.
• Exploration of issues related to adverse event data and interrelationships among outcomes.

Main Results:

• Prespecifying outcome priorities enhances trial integrity but should not stifle exploration.
• Corrections for multiple testing have limited value; focus on clinical relevance.
• Combined outcomes and global tests can be useful but require careful justification.
• Small trial sizes and overemphasis on p-values exacerbate problems.

Conclusions:

• Effective management of multiple outcomes is crucial for robust clinical trial evidence.
• A balanced approach is needed, combining prespecified plans with openness to unexpected findings.
• Addressing issues in trial design, analysis, and reporting improves the reliability of clinical trial results.