Personalized treatment plans with multivariate outcomes

Area of Science:

• Biostatistics
• Medical Informatics
• Clinical Trial Design

Background:

• Individualized treatment selection is challenging with multivariate responses.
• Existing methods may not accommodate a wide range of models or numerous treatments.

Purpose of the Study:

• To propose a novel, flexible method for individualized treatment selection with multivariate outcomes.
• To establish an ordering of treatments based on performance measures using a Mahalanobis-type distance.

Main Methods:

• Utilizes single index models to approximate conditional mean responses based on patient covariates.
• Employs smoothed estimates of conditional means to construct a distance measure for treatment comparison.
• Applies the distance measure to estimate the optimal treatment for individuals.

Main Results:

• The proposed method demonstrates effective performance in finite sample simulations.
• The empirical study validates the method's utility in ranking treatments for individualized selection.
• The approach is applicable to various statistical models and any number of treatments.

Conclusions:

• The novel method provides a robust framework for optimal treatment selection in multivariate settings.
• The Mahalanobis-type distance measure effectively guides treatment choice based on predicted patient response.
• The method's applicability is confirmed through simulation and real-world HIV clinical trial data analysis.