Sparse Concordance-assisted Learning for Optimal Treatment Decision
Sparse Concordance-assisted Learning for Optimal Treatment Decision
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发表时间：
2018-04
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通讯作者：
Shuhan Liang;Wenbin Lu;R. Song;Lan Wang
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作者：
Shuhan Liang;Wenbin Lu;R. Song;Lan Wang
为了寻找最优决策规则，Fan等人提出了最优决策规则。(2016)提出了一种创新的基于最大秩相关估计器的一致性辅助学习算法。它通过两两比较，更好地利用了现有的信息。然而，目标函数是不连续的，在计算上很难优化。在这篇文章中，我们考虑了一个凸的代理损失函数来解决这个问题。此外，我们的算法保证了决策规则的稀疏性，并且易于解释。给出了超高维情形下估计系数的L 2误差界。各种设置的仿真结果和对STAR*D的应用都表明，在协变量数目较大的情况下，该方法仍然能够成功地估计出最优治疗方案。
To find optimal decision rule, Fan et al. (2016) proposed an innovative concordance-assisted learning algorithm which is based on maximum rank correlation estimator. It makes better use of the available information through pairwise comparison. However the objective function is discontinuous and computationally hard to optimize. In this paper, we consider a convex surrogate loss function to solve this problem. In addition, our algorithm ensures sparsity of decision rule and renders easy interpretation. We derive the L 2 error bound of the estimated coefficients under ultra-high dimension. Simulation results of various settings and application to STAR*D both illustrate that the proposed method can still estimate optimal treatment regime successfully when the number of covariates is large.