Doubly Robust Estimation of Optimal Dynamic Treatment Regimes.
Doubly Robust Estimation of Optimal Dynamic Treatment Regimes.
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DOI：
10.1007/s12561-013-9097-6
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发表时间：
2014
影响因子：
1
通讯作者：
Rosthoj, Susanne
中科院分区：
文献类型：
--
作者：
Barrett, Jessica K;Henderson, Robin;Rosthoj, Susanne
我们比较了从观测数据估计最优动态决策规则的方法，特别关注估计Murphy定义的后悔函数(在J.R.Stat.社会，爵士。B，Stat.美托洛尔。65：331-355，)。我们提出了Almirall等人的后悔回归方法的双重稳健版本。(在Biometrics 66：131-139中)和Henderson等人。(在Biometrics 66：1192-1201中)，并证明它相当于Robins的有效g估计过程的简化形式(Robins，在第二届生物统计学研讨会论文集上)。施普林格，纽约，第189-326页，()。模拟研究表明，虽然后悔回归方法在没有模型错误指定的情况下是最有效的，但在存在错误指定的情况下，有效的g-估计过程更稳健。然而，g估计方法在复杂情况下可能很难应用。我们通过一个长期抗凝患者控制凝血时间的应用，说明了控制凝血时间的思路和方法。
We compare methods for estimating optimal dynamic decision rules from observational data, with particular focus on estimating the regret functions defined by Murphy (in J. R. Stat. Soc., Ser. B, Stat. Methodol. 65:331–355,). We formulate a doubly robust version of the regret-regression approach of Almirall et al. (in Biometrics 66:131–139, ) and Henderson et al. (in Biometrics 66:1192–1201, ) and demonstrate that it is equivalent to a reduced form of Robins’ efficient g-estimation procedure (Robins, in Proceedings of the Second Symposium on Biostatistics. Springer, New York, pp. 189–326, ). Simulation studies suggest that while the regret-regression approach is most efficient when there is no model misspecification, in the presence of misspecification the efficient g-estimation procedure is more robust. The g-estimation method can be difficult to apply in complex circumstances, however. We illustrate the ideas and methods through an application on control of blood clotting time for patients on long term anticoagulation.