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Entropy Learning for Dynamic Treatment Regimes
Statistica Sinica ( IF 1.4 ) Pub Date : 2020-01-01 , DOI: 10.5705/ss.202018.0076
Binyan Jiang 1 , Rui Song 2 , Jialiang Li 3 , Donglin Zeng 2
Affiliation  

Estimating optimal individualized treatment rules (ITRs) in single or multi-stage clinical trials is one key solution to personalized medicine and has received more and more attention in statistical community. Recent development suggests that using machine learning approaches can significantly improve the estimation over model-based methods. However, proper inference for the estimated ITRs has not been well established in machine learning based approaches. In this paper, we propose a entropy learning approach to estimate the optimal individualized treatment rules (ITRs). We obtain the asymptotic distributions for the estimated rules so further provide valid inference. The proposed approach is demonstrated to perform well in finite sample through extensive simulation studies. Finally, we analyze data from a multi-stage clinical trial for depression patients. Our results offer novel findings that are otherwise not revealed with existing approaches.

中文翻译:

动态治疗方案的熵学习

在单阶段或多阶段临床试验中估计最佳个体化治疗规则 (ITR) 是个性化医疗的关键解决方案之一,并且越来越受到统计学界的关注。最近的发展表明,使用机器学习方法可以显着改善基于模型的方法的估计。然而,在基于机器学习的方法中尚未很好地建立对估计 ITR 的正确推断。在本文中,我们提出了一种熵学习方法来估计最佳个性化治疗规则(ITR)。我们获得了估计规则的渐近分布,以便进一步提供有效的推理。通过广泛的模拟研究,证明所提出的方法在有限样本中表现良好。最后,我们分析了抑郁症患者多阶段临床试验的数据。我们的结果提供了现有方法无法揭示的新发现。
更新日期:2020-01-01
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