The Relative Performance of Targeted Maximum Likelihood Estimators
The Relative Performance of Targeted Maximum Likelihood Estimators
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DOI：
10.2202/1557-4679.1308
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发表时间：
2011-01-01
影响因子：
1.2
通讯作者：
Sekhon, Jasjeet S.
中科院分区：
文献类型：
--
作者：
Porter, Kristin E.;Gruber, Susan;Sekhon, Jasjeet S.
有一个积极的辩论在文献中的删失数据的相对性能模型为基础的最大似然估计，IPCW估计，和各种双稳健半参数有效估计。Kang和Schafer（2007）在一个模拟研究中证明了双重稳健估计和IPCW估计的脆弱性。他们专注于一个简单的缺失数据问题与协变量，其中一个人希望估计的结果，是受missingness的平均值。Robins等人（2007）、Tsiatis和Davidian（2007）、Tan（2007）以及里奇韦和McCaffrey（2007）的回应进一步探讨了双稳健估计量面临的挑战，并提供了改善其稳定性的建议。在这篇文章中，我们加入辩论提出有针对性的最大似然估计（TMLE）。我们证明了TMLE，保证参数的子模型采用TMLE程序尊重的连续结果的全局边界，特别适合于处理积极的违规行为，因为除了双稳健和半参数有效，他们是替代估计。我们证明了TMLE相对于其他估计器的实际性能，在Kang和Schafer（2007）设计的模拟中，以及在具有更大估计挑战的修改后的模拟中。
There is an active debate in the literature on censored data about the relative performance of model based maximum likelihood estimators, IPCW-estimators, and a variety of double robust semiparametric efficient estimators. Kang and Schafer (2007) demonstrate the fragility of double robust and IPCW-estimators in a simulation study with positivity violations. They focus on a simple missing data problem with covariates where one desires to estimate the mean of an outcome that is subject to missingness. Responses by Robins, et al. (2007), Tsiatis and Davidian (2007), Tan (2007) and Ridgeway and McCaffrey (2007) further explore the challenges faced by double robust estimators and offer suggestions for improving their stability. In this article, we join the debate by presenting targeted maximum likelihood estimators (TMLEs). We demonstrate that TMLEs that guarantee that the parametric submodel employed by the TMLE procedure respects the global bounds on the continuous outcomes, are especially suitable for dealing with positivity violations because in addition to being double robust and semiparametric efficient, they are substitution estimators. We demonstrate the practical performance of TMLEs relative to other estimators in the simulations designed by Kang and Schafer (2007) and in modified simulations with even greater estimation challenges.