Subgroup identification from randomized clinical trial data.
Subgroup identification from randomized clinical trial data.
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DOI：
10.1002/sim.4322
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发表时间：
2011-10-30
影响因子：
2
通讯作者：
Ruberg, Stephen J.
中科院分区：
文献类型：
--
作者：
Foster, Jared C.;Taylor, Jeremy M. G.;Ruberg, Stephen J.
我们考虑在随机临床试验中确定可能具有增强治疗效果的患者亚组的问题，并且希望该亚组由有限数量的协变量定义。对于这个问题，制定一个标准的、预先确定的策略可能有助于避免众所周知的子群分析的危险。我们提出了一种方法来发现增强治疗效果的亚组。这种方法被称为“虚拟双胞胎”，包括预测每个受试者对治疗和控制“双胞胎”的反应概率。然后将这些概率的差异用作分类树或回归树的结果，这可能包括任何一组协变量。我们将测度Q（Â）定义为估计亚组Â的治疗效果与边际治疗效果之间的差值。我们提出了几种用于获得Q估计的方法（Â），包括使用原始数据中的估计概率来估计Q（Â），使用新模拟数据中的估计概率，两种基于交叉验证的方法和基于bootstrap的偏差校正方法。仿真研究结果表明，当存在增强治疗效果的真实子组时，虚拟双胞胎方法明显优于具有正向选择的逻辑回归方法。一般情况下，亚群估计需要较大的样本量或较强的强化处理效果。作为说明，我们将提出的方法应用于随机临床试验的数据。
We consider the problem of identifying a subgroup of patients who may have an enhanced treatment effect in a randomized clinical trial, and it is desirable that the subgroup be defined by a limited number of covariates. For this problem, the development of a standard, pre-determined strategy may help to avoid the well-known dangers of subgroup analysis. We present a method developed to find subgroups of enhanced treatment effect. This method, referred to as “Virtual Twins”, involves predicting response probabilities for treatment and control “twins” for each subject. The difference in these probabilities is then used as the outcome in a classification or regression tree, which can potentially include any set of the covariates. We define a measure Q(Â) to be the difference between the treatment effect in estimated subgroup  and the marginal treatment effect. We present several methods developed to obtain an estimate of Q(Â), including estimation of Q(Â) using estimated probabilities in the original data, using estimated probabilities in newly simulated data, two cross-validation-based approaches and a bootstrap-based bias corrected approach. Results of a simulation study indicate that the Virtual Twins method noticeably outperforms logistic regression with forward selection when a true subgroup of enhanced treatment effect exists. Generally, large sample sizes or strong enhanced treatment effects are needed for subgroup estimation. As an illustration, we apply the proposed methods to data from a randomized clinical trial.
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影响因子：
1.1
作者：
Cui, Lu;Hung, H M James;Tsong, Yi
通讯作者：
Tsong, Yi
影响因子：
2.7
作者：
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通讯作者：
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作者：
Freidlin, Boris;Jiang, Wenyu;Simon, Richard
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影响因子：
120.7
作者：
YUSUF, S;WITTES, J;TYROLER, HA
通讯作者：
TYROLER, HA
DOI：
10.2202/1557-4679.1071
发表时间：
2008-01-28
期刊：
The international journal of biostatistics
影响因子：
--
作者：
Su, Xiaogang;Zhou, Tianni;Yang, Song
通讯作者：
Yang, Song