Selection of the Optimal Personalized Treatment from Multiple Treatments with Right-censored Multivariate Outcome Measures

TL;DR

This paper introduces a new method for selecting the best personalized treatment when responses are multivariate and may include right-censored data, applicable to various models and treatments.

Contribution

It develops a semiparametric approach combining patient-specific scores, nonparametric estimation, and rank aggregation to handle complex, censored multivariate outcomes for treatment selection.

Findings

Effective in finite sample simulations

Handles right-censored survival data

Demonstrated on HIV clinical trial data

Abstract

We propose a novel personalized concept for the optimal treatment selection for a situation where the response is a multivariate vector, that could contain right-censored variables such as survival time. The proposed method can be applied with any number of treatments and outcome variables, under a broad set of models. Following a working semiparametric Single Index Model that relates covariates and responses, we first define a patient-specific composite score, constructed from individual covariates. We then estimate conditional means of each response, given the patient score, correspond to each treatment, using a nonparametric smooth estimator. Next, a rank aggregation technique is applied to estimate an ordering of treatments based on ranked lists of treatment performance measures given by conditional means. We handle the right-censored data by incorporating the inverse probability of censoring weighting to the corresponding estimators. An empirical study illustrates the performance of the proposed method in finite sample problems. To show the applicability of the proposed procedure for real data, we also present a data analysis using HIV clinical trial data, that contained a right-censored survival event as one of the endpoints.