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.
• Detecting patient subgroups with higher treatment benefit or lack thereof is essential.

Purpose of the Study:

• To develop and present the subscreen R package for detailed clinical trial population analysis.
• To efficiently calculate point estimates for multiple subgroups and identify those differing from overall trial results.

Main Methods:

• The subscreen R package calculates point estimates (e.g., hazard ratios) for numerous subgroups.
• The approach focuses on exploratory results, avoiding inferential statistics like P values or confidence intervals.
• The application was validated using two clinical study datasets and a simulation study.

Main Results:

• Visualization of combined subgroups effectively illustrates homogeneity or heterogeneity of estimates.
• The tool facilitates targeted planning for future clinical trials based on subgroup insights.

Conclusions:

• The subscreen approach aligns with EMA guidance on subgroup analyses in confirmatory trials.
• Provides a user-friendly yet powerful tool to address the need for spontaneous subgroup effect investigations.
• Facilitates efficient interdisciplinary discussions on clinical trial results.