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Activity Number: 466 - Personalized/Precision Medicine I
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #301861
Title: Personalized Treatment Selection Using Data from Crossover Designs with Carry Over Effects
Author(s): Chathura Siriwardhana* and K.B. Kulasekera and Somnath Datta
Companies: University of Hawaii and University of Louisville and University of Florida
Keywords: Crossover Designs; Design variables; Personalized Treatments; Single Index Models; Precision Medicine

In this work, we propose a semi-parametric method for estimating the optimal treatment for a given patient based on individual covariate information for that patient when data from a crossover design are available. Here we assume there are carryover effects for patients switching from one treatment to another. For the K treatment (K< =2) scenario, we show that nonparametric estimation of carryover effects can have the undesirable property that comparison of treatment means can only be done using independent outcome measurements from different groups of patients rather than using available joint measurements for each patient. To overcome this barrier we compare probabilities of outcome variable of each treatment dominating outcome variables for all other treatments conditional on patient-specific scores constructed from patient covariates. We suggest Single Index Models as appropriate models connecting outcome variables to covariates and our empirical investigations show that frequencies of correct treatment assignments are highly accurate. 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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