经验效率最大化:随机实验和生存分析中改进的局部有效协变量调整

IF 1.2 4区 数学
D. Rubin, M. J. van der Laan
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引用次数: 101

摘要

人们早就认识到协变量调整可以提高随机实验的精度,即使它不是严格必要的。当一个离散的协变量将样本划分为几个层时,调整通常是直接的,但即使是一个连续的协变量,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,信息时代促进了大量基线信息的收集,在可预见的未来,是否以及如何调整协变量的长期问题可能会让研究人员参与其中。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,在对感兴趣的参数的估计方程中给出一个讨厌的参数拟合。通常的广告是,如果工作模型是正确的,估计器将是渐近有效的,但否则仍然是一致的和渐近高斯的。然而,在协变量调整问题中,通过将标准的基于似然的拟合应用于错误指定的工作模型,人们可以很好地估计感兴趣的参数。我们提出了一种新的方法,经验效率最大化,以优化工作模型拟合的结果参数估计。除了随机实验设置外,我们还展示了协变量调整程序如何用于生存分析应用。数值渐近效率计算证明了相对于标准局部有效估计的增益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Empirical Efficiency Maximization: Improved Locally Efficient Covariate Adjustment in Randomized Experiments and Survival Analysis
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.
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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics Mathematics-Statistics and Probability
CiteScore
2.30
自引率
8.30%
发文量
28
期刊介绍: The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.
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