Finding the best subgroup with differential treatment effect with multiple outcomes

Area of Science:

• Biostatistics
• Clinical Trial Analysis
• Precision Medicine

Background:

• Precision medicine seeks to tailor treatments to patient subgroups for maximum benefit.
• Current subgroup analysis methods often focus on single outcomes, neglecting complex, multi-outcome scenarios.
• Identifying the 'best' subgroup requires balancing treatment effects across diverse outcome types.

Purpose of the Study:

• To introduce a novel definition for identifying the optimal subgroup in precision medicine under a multiple-outcome setting.
• To develop a method that accounts for continuous, binary, and time-to-event outcomes simultaneously.
• To enable a trade-off between subgroup size and treatment effect across various outcomes.

Main Methods:

• Proposed a new definition for the best subgroup in multiple-outcome settings.
• Incorporated a trade-off between subgroup size and conditional average treatment effects (CATE) for each outcome.
• Accounted for the relative importance or contribution of different outcomes.

Main Results:

• Simulations demonstrated the utility and application of the proposed definition.
• Analysis of the RIVUR clinical trial identified a specific subgroup of children benefiting from long-term antimicrobial prophylaxis.
• The definition successfully balanced treatment effects across urinary tract infection and renal scarring outcomes.

Conclusions:

• The proposed definition advances subgroup identification in precision medicine by handling multiple, diverse outcomes.
• This approach allows for a more nuanced and comprehensive identification of patient subgroups who benefit most from interventions.
• Application to the RIVUR trial highlights potential clinical utility in optimizing treatment strategies for pediatric conditions.