Selection of the Optimal Personalized Treatment from Multiple Treatments with Multivariate Outcome Measures
Wednesday, November 7, 2018 - 4:00pm - 4:30pm
n this work we propose a novel method for individualized treatment selection when the treatment response is multivariate. For the K treatment (K greater than or equal to 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 multiple treatments and outcome variables, and it can be applied for a broad set of models. The proposed method uses a rank aggregation technique to establish an ordering of treatments based on ranked lists of treatment performance measures such as smooth conditional means and conditional probability of a response for one treatment dominating others. 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. We also present a data analysis using a HIV clinical trial data to illustrate the proposed procedure.