Multiplicity issues in exploratory subgroup analysis

Area of Science:

• Clinical trial methodology
• Biostatistics
• Personalized medicine

Background:

• Subgroup identification is crucial for developing tailored therapies and personalized medicine.
• Exploratory subgroup analysis in clinical trials has faced criticism for unreliability.
• Advances in machine learning offer principled approaches to subgroup identification.

Purpose of the Study:

• To review multiplicity issues in exploratory subgroup analysis.
• To illustrate multiplicity corrections using SIDES methods.
• To present a case study of subgroup search in an oncology trial.

Main Methods:

• Review of multiplicity issues in exploratory subgroup analysis.
• Illustration of multiplicity corrections with SIDES (subgroup identification based on differential effect search) methods.
• Application of subgroup search algorithms with resampling-based multiplicity adjustments in a Phase III oncology trial.

Main Results:

• Principled subgroup search methods, incorporating multiplicity adjustments, enhance the reliability of exploratory analyses.
• SIDES methods provide a framework for addressing selection bias in subgroup identification.
• Resampling-based multiplicity adjustments are effective in subgroup search algorithms.

Conclusions:

• Addressing multiplicity is essential for robust subgroup identification in clinical trials.
• Principled subgroup search, like SIDES, improves the validity of personalized medicine approaches.
• The presented methods and case study offer practical guidance for reliable subgroup analysis.