Optimal Personalized Treatment Selection with Multivariate Outcome Measures in a Multiple Treatment Case.

Area of Science:

• Biostatistics
• Clinical Trial Design
• Personalized Medicine

Background:

• Individualized treatment selection is complex, especially with multiple correlated outcomes.
• Existing methods may not adequately address the nuances of correlated treatment responses.

Purpose of the Study:

• To propose a novel, flexible method for individualized treatment selection with correlated multiple responses.
• To develop a technique applicable to any number of treatments and outcome variables across various models.

Main Methods:

• Utilizes patient-specific scores derived from covariate measurements.
• Employs a rank aggregation technique accounting for correlations in ranked treatment lists.
• Compares treatment performance using measures like the smooth conditional mean.

Main Results:

• The proposed method effectively orders treatments based on performance measures.
• Demonstrates robust performance in simulation studies for finite samples.
• Successfully applied to real-world HIV clinical trial data.

Conclusions:

• The novel rank aggregation method provides a robust approach for individualized treatment selection.
• The technique facilitates personalized treatment decisions by integrating patient and clinician preferences.
• Applicable to diverse clinical scenarios involving multiple correlated treatment responses.