Quantiles based personalized treatment selection for multivariate outcomes and multiple treatments

Abstract

In this work, we propose a method for individualized treatment selection when there are correlated multiple responses for the $K$ treatment ( $K\ge 2$ ) scenario. Here we use ranks of quantiles of 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 using any number of quantiles and it can be applied for a broad set of models. We propose a rank aggregation technique for combining several lists of ranks where both these lists and elements within each list can be correlated. 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 illustrations using two different datasets from diabetes and HIV-1 clinical trials to show the applicability of the proposed procedure for real data.

Keywords: design variables; personalized treatments; quantiles of outcomes; rank aggregation; single index models.