Studying treatment-effect heterogeneity in precision medicine through induced subgroups

Area of Science:

• Biostatistics
• Medical Informatics
• Clinical Trial Design

Background:

• Precision medicine aims to tailor treatments based on patient characteristics, requiring evidence of differential treatment response in patient subgroups.
• Identifying these treatment-subgroup interactions is key, but a priori hypotheses are often lacking or incomplete.
• Post hoc methods are needed to discover these subgroups from empirical data.

Purpose of the Study:

• To discuss major concepts and considerations for using data-driven methods to identify patient subgroups with differential treatment responses.
• To provide a systematic, conceptual, and technical analysis of relevant research questions and data handling capabilities.
• To review empirical evidence and illustrate methods with a real-world dataset.

Main Methods:

• Conceptual and technical analysis of statistical methods for subgroup identification.
• Review of existing literature and empirical evidence on post hoc subgroup discovery.
• Application and illustration using a dataset comparing antidepressant treatments.

Main Results:

• Discussion of the challenges and choices involved in applying post hoc subgroup identification methods.
• Systematic analysis of research questions, data types, and software for these methods.
• Illustrative analysis of antidepressant treatment effectiveness across identified subgroups.

Conclusions:

• Methods for post hoc identification of treatment-subgroup interactions are essential for evidence-based precision medicine.
• Careful consideration of analytical choices is necessary for reliable subgroup discovery.
• Empirical experience and clear guidance are needed for medical statisticians and researchers using these advanced methods.