A general statistical framework for subgroup identification and comparative treatment scoring.
A general statistical framework for subgroup identification and comparative treatment scoring.
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作者：
Chen S;Tian L;Cai T;Yu M
最近开发了许多统计方法来识别可能从不同可用治疗中获益的患者亚组。与传统的结果建模方法相比，这些方法侧重于建模处理和协变量之间的相互作用，而忽略或最小化建模协变量的主效应，因为亚组识别仅取决于相互作用的符号。然而，这些方法是分散的，而且往往范围狭窄。在本文中，我们提出了一个总体框架，加权和A-学习，在随机临床试验和观察性研究中的亚组识别。我们的框架涉及最小建模的结果和协变量之间的关系相关的亚组识别。在此框架下，我们还可以估计交互作用的大小，从而构建衡量个体化治疗效果的评分系统。所提出的方法是相当灵活的，包括许多最近提出的估计作为特殊情况。因此，最初为随机临床试验提出的一些估计量可以扩展到观察性研究，基于加权方法的程序可以转换为A学习方法，反之亦然。我们的方法还允许直接合并高维数据的正则化方法，以及可能的效率增强和推广到多种治疗。我们通过广泛的数值研究，研究属于所提出的框架的几个程序的实证性能。
Many statistical methods have recently been developed for identifying subgroups of patients who may benefit from different available treatments. Compared with the traditional outcome-modeling approaches, these methods focus on modeling interactions between the treatments and covariates while by-pass or minimize modeling the main effects of covariates because the subgroup identification only depends on the sign of the interaction. However these methods are scattered and often narrow in scope. In this paper, we propose a general framework, by weighting and A-learning, for subgroup identification in both randomized clinical trials and observational studies. Our framework involves minimum modeling for the relationship between the outcome and covariates pertinent to the subgroup identification. Under the proposed framework, we may also estimate the magnitude of the interaction, which leads to the construction of scoring system measuring the individualized treatment effect. The proposed methods are quite flexible and include many recently proposed estimators as special cases. As a result, some estimators originally proposed for randomized clinical trials can be extended to observational studies, and procedures based on the weighting method can be converted to an A-learning method and vice versa. Our approaches also allow straightforward incorporation of regularization methods for high-dimensional data, as well as possible efficiency augmentation and generalization to multiple treatments. We examine the empirical performance of several procedures belonging to the proposed framework through extensive numerical studies.
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影响因子：
1.9
作者：
Zhang B;Tsiatis AA;Laber EB;Davidian M
通讯作者：
Davidian M
影响因子：
1.4
作者：
McKeague IW;Qian M
通讯作者：
Qian M
将统计相互作用的强大推断倍增。
DOI：
10.1198/016214508000001084
发表时间：
2008-12-01
影响因子：
3.7
作者：
Vansteelandt S;Vanderweele TJ;Robins JM
通讯作者：
Robins JM
影响因子：
2
作者：
Foster, Jared C.;Taylor, Jeremy M. G.;Ruberg, Stephen J.
通讯作者：
Ruberg, Stephen J.
影响因子：
4.5
作者：
Qian M;Murphy SA
通讯作者：
Murphy SA