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 n1/3-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 effectiveness of the proposed methodology are demonstrated by simulation studies and an application to an AIDS data.

中文翻译：

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协和辅助学习，用于估计最佳个性化治疗方案。

在本文中，我们提出了一种新的一致性辅助学习方法，用于估计最佳的个性化治疗方案。我们首先介绍一种用于处方治疗的协调函数，并提出一种用于估计协调函数的鲁棒秩次回归方法。然后，我们找到达到阈值的治疗方案，以最大程度地提高协调功能，称为规范索引。最后，在使协调功能最大化的治疗方案类别中，我们找到了使价值函数最大化的最佳阈值。我们建立了针对规范索引中参数估计器的收敛速度和渐近正态性。开发了一种诱导平滑方法来估计所提出估计量的渐近方差。我们还建立了估计的最佳阈值及其极限分布的n1 / 3一致性。此外，在一类单调索引模型下，开发了一种在指标索引中具有双重鲁棒性的参数估计器。通过仿真研究和对艾滋病数据的应用证明了所提出方法的实际用途和有效性。