A Systematic Approach for Post Hoc Subgroup Analyses With Applications in Clinical Case Studies

Area of Science:

• Clinical trial methodology
• Biostatistics
• Pharmacovigilance

Background:

• Subgroup analysis in clinical trials is crucial for identifying differential treatment effects across patient populations.
• Detecting patient subgroups that benefit most or least from a treatment is essential for personalized medicine.

Purpose of the Study:

• To develop a software application (R package) for detailed analysis of clinical trial populations.
• To efficiently calculate point estimates for multiple subgroups to identify those differing from overall trial results.
• To provide a tool that supports exploratory analyses and encourages evidence-based discussions.

Main Methods:

• The subscreen R package was developed to analyze clinical trial data at a granular level.
• It calculates point estimates (e.g., hazard ratios) for various subgroups without using inferential statistics like P-values.
• The application was validated using two clinical study datasets and a simulation study.

Main Results:

• The software visualizes the homogeneity or heterogeneity of subgroup estimates relative to the overall trial result.
• This visualization facilitates a clearer understanding of treatment effect variations across different patient groups.
• The application demonstrated its utility in identifying potentially significant subgroup differences.

Conclusions:

• The subscreen application supports the growing need for rigorous subgroup effect investigation, aligning with regulatory guidelines (e.g., EMA).
• It addresses the lack of accessible tools for interdisciplinary teams to explore subgroup effects spontaneously.
• This powerful and user-friendly tool facilitates more targeted future trial design and planning.