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Activity Number: 464 - New Directions in Personalized Treatment Selection
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: International Indian Statistical Association
Abstract #328625 Presentation
Title: A Probability Based Method for Selecting the Optimal Personalized Treatment from Multiple Treatments
Author(s): Karunarathna B Kulasekera* and Chathura Siriwardhana and Somnath Datta
Companies: University of Louisville and University of Hawaii and University of Florida
Keywords: Single Index Models; Design variables; Personalized Treatments

In this work we propose a method for optimal treatment assignment based on individual covariate information for a patient. For the K treatment scenario we compare quantities that are suitable surrogates to true conditional probabilities of outcome variable of each treatment dominating outcome variables for all other treatments conditional on patient specific scores constructed from each patient's covariates. As opposed to methods based on conditional means, our method can be applied for a broad set of models and error structures. Furthermore, the proposed method has very desirable large sample properties. We suggest Single Index Models as appropriate models connecting outcome variables to covariates and our empirical investigations show that correct treatment assignments are highly accurate. Furthermore, selection of a treatment using the proposed metric appears to incur no losses in terms of the average reward for cases when two treatments are close in terms of this metric. We also conduct a real data analysis to show the applicability of the proposed procedure.

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

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