Optimal personalized treatment selection with multivariate outcome measures in a multiple treatment case

Abstract

In this work we propose a novel method for individualized treatment selection when there are correlated multiple treatment responses. For the K treatment ($K\ge 2$) scenario, we compare quantities that are suitable indexes based on outcome variables for each treatment conditional on patient-specific scores constructed from collected covariate measurements. Our method covers any number of treatments and outcome variables, and it can be applied for a broad set of models. The proposed method uses a rank aggregation technique that takes into account possible correlations among ranked lists to estimate an ordering of treatments based on treatment performance measures such as the smooth conditional mean. The method has the flexibility to incorporate patient and clinician preferences into the optimal treatment decision on an individual case basis. A simulation study demonstrates the performance of the proposed method in finite samples. We also present data analyses using HIV clinical trial data to show the applicability of the proposed procedure for real data.

Keywords:

• Design variables
• Personalized treatments
• Rank aggregation
• Single index models

Acknowledgments

The research work by Chathura Siriwardhana was partially supported by U54MD007601 grant from the National Institutes of Health.

Data availability statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.