Covariate adjustment in randomized trials with binary outcomes: targeted maximum likelihood estimation.
Covariate adjustment in randomized trials with binary outcomes: targeted maximum likelihood estimation.
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DOI：
10.1002/sim.3445
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发表时间：
2009-01-15
影响因子：
2
通讯作者：
van der Laan, M. J.
中科院分区：
文献类型：
--
作者：
Moore, K. L.;van der Laan, M. J.
随机试验中使用线性模型对连续结果进行协变量调整，在估计治疗的边际效应方面比未调整的方法更有效。然而，对于二元结果，研究人员通常依赖于未经调整的估计，因为文献表明基于逻辑回归模型的协变量调整估计效率较低。在调整协变量时遗漏的关键步骤是，为了获得边际效应，必须对这些协变量的调整估计进行积分/平均。我们应用目标最大似然估计（tMLE）方法，对二元结果使用协变量调整来获得边际效应的估计量。我们表明，随机试验中使用逻辑回归模型的协变量调整可以通过对协变量进行平均来映射，以获得完全稳健和有效的边际效应估计量，它等于目标最大似然估计量。这个tMLE是通过简单地向固定的初始回归添加一个聪明的协变量来获得的。我们提出的模拟研究表明，这种tMLE比未调整的方法提高了效率和功率，特别是对于较小的样本量，即使在回归模型指定错误的情况下。
Covariate adjustment using linear models for continuous outcomes in randomized trials has been shown to increase efficiency and power over the unadjusted method in estimating the marginal effect of treatment. However, for binary outcomes, investigators generally rely on the unadjusted estimate as the literature indicates that covariate-adjusted estimates based on the logistic regression models are less efficient. The crucial step that has been missing when adjusting for covariates is that one must integrate/average the adjusted estimate over those covariates in order to obtain the marginal effect. We apply the method of targeted maximum likelihood estimation (tMLE) to obtain estimators for the marginal effect using covariate adjustment for binary outcomes. We show that the covariate adjustment in randomized trials using the logistic regression models can be mapped, by averaging over the covariate(s), to obtain a fully robust and efficient estimator of the marginal effect, which equals a targeted maximum likelihood estimator. This tMLE is obtained by simply adding a clever covariate to a fixed initial regression. We present simulation studies that demonstrate that this tMLE increases efficiency and power over the unadjusted method, particularly for smaller sample sizes, even when the regression model is mis-specified.
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