Empirical Efficiency Maximization: Improved Locally Efficient Covariate Adjustment in Randomized Experiments and Survival Analysis
Empirical Efficiency Maximization: Improved Locally Efficient Covariate Adjustment in Randomized Experiments and Survival Analysis
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DOI：
10.2202/1557-4679.1084
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发表时间：
2008-05
期刊：
影响因子：
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通讯作者：
D. Rubin;M. J. van der Laan
中科院分区：
文献类型：
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作者：
D. Rubin;M. J. van der Laan
人们早就认识到，协变量调整可以提高随机实验的精度，即使这并不是绝对必要的。当离散协变量将样本划分为几个层时，调整通常很简单，但即使是单个连续协变量（例如年龄）也会变得更加复杂。由于随机实验仍然是科学探究的黄金标准，而信息时代有利于大量收集基线信息，因此在可预见的未来，是否以及如何调整协变量这一长期存在的问题可能会吸引研究者的关注。 在 James Robins 及其合作者为一般粗化数据结构引入的局部有效估计方法中，首先拟合一个相对较小的工作模型，通常具有最大似然性，在感兴趣参数的估计方程中给出一个令人讨厌的参数拟合。通常的广告是，如果工作模型正确，估计器将渐近有效，但否则仍将是一致的且渐近高斯分布。然而，通过将标准的基于似然的拟合应用于协变量调整问题中错误指定的工作模型，人们可能很难估计出感兴趣的参数。我们提出了一种新方法，即经验效率最大化，以优化适合结果参数估计的工作模型。除了随机实验设置之外，我们还展示了如何在生存分析应用中使用我们的协变量调整程序。数值渐近效率计算证明了相对于标准局部有效估计器的增益。
It has long been recognized that covariate adjustment can increase precision in randomized experiments, even when it is not strictly necessary. Adjustment is often straightforward when a discrete covariate partitions the sample into a handful of strata, but becomes more involved with even a single continuous covariate such as age. As randomized experiments remain a gold standard for scientific inquiry, and the information age facilitates a massive collection of baseline information, the longstanding problem of if and how to adjust for covariates is likely to engage investigators for the foreseeable future.In the locally efficient estimation approach introduced for general coarsened data structures by James Robins and collaborators, one first fits a relatively small working model, often with maximum likelihood, giving a nuisance parameter fit in an estimating equation for the parameter of interest. The usual advertisement is that the estimator will be asymptotically efficient if the working model is correct, but otherwise will still be consistent and asymptotically Gaussian.However, by applying standard likelihood-based fits to misspecified working models in covariate adjustment problems, one can poorly estimate the parameter of interest. We propose a new method, empirical efficiency maximization, to optimize the working model fit for the resulting parameter estimate.In addition to the randomized experiment setting, we show how our covariate adjustment procedure can be used in survival analysis applications. Numerical asymptotic efficiency calculations demonstrate gains relative to standard locally efficient estimators.