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Activity Number: 483 - Statistical Approaches in Precision Medicine
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Biometrics Section
Abstract #312238
Title: Selection of the Optimal Personalized Treatment from Multiple Treatments with Right-Censored Multivariate Outcome Measures
Author(s): Chathura Siriwardhana* and Somnath Datta and KB Kulasekera
Companies: University of Hawaii and University of Florida and University of Louisville
Keywords: Personalized Treatments; Right-censoring; Rank Aggregation; Single Index Models; Design variables

In this work, we propose a novel method for individualized treatment selection when the treatment response is multivariate. We allow some of the outcome variables to be right-censored measures such as survival time. 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 to estimate an ordering of treatments based on ranked lists of treatment performance measures given by smooth conditional means. We handle the right-censored data by incorporating the inverse probability of censoring weighting criteria to the corresponding estimators. The method has the flexibility to incorporate patient and clinician preferences to the optimal treatment decision on an individual case basis. An empirical study demonstrates the performance of the proposed method in finite samples. To show the applicability of the proposed procedure for real data, we also present a data analysis using an HIV clinical trial data, that contained a right-censored survival event as one of the endpoints.

Authors who are presenting talks have a * after their name.

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