Concordance-Assisted Learning for Estimating Optimal Individualized Treatment Regimes

• Activity Number: 625
• Type: Invited
• Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
• Sponsor: ENAR
• Author(s): Caiyun Fan and Wenbin Lu and Rui Song* and Yong Zhou
• Companies: Shanghai University of Finance and Economics and North Carolina State University and North Carolina State University and Shanghai University of Finance and Economics

Abstract

We propose a new concordance-assisted learning for estimating optimal individualized treatment regimes. We first introduce a type of concordance function for prescribing treatment and propose a robust rank regression method for estimating the concordance function. We then nd treatment regimes, up to a threshold, to maximize the concordance function, named prescriptive index. Finally, within the class of treatment regimes that maximize the concordance function, we nd the optimal threshold to maximize the value function. We establish the convergence rate and asymptotic normality of the proposed estimator for parameters in the prescriptive index. An induced smoothing method is developed to estimate the asymptotic variance of the proposed estimator. We also establish the cubit root consistency of the estimated optimal threshold and its limiting distribution. In addition, a doubly robust estimator of parameters in the prescriptive index is developed under a class of monotonic index models. The practical use and eectiveness of the proposed methodology are demonstrated by simulation studies and an application to an AIDS data.