Concordance-Assisted Learning for Estimating Optimal Individualized Treatment Regimes.

ABSTRACT: In this article, 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 find 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 find 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 n

SUBMITTER: Fan C 

PROVIDER: S-EPMC5774868 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature