A regression tree approach to identifying subgroups with differential treatment effects.

A regression tree approach to identifying subgroups with differential treatment effects.
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回归树方法,用于识别具有差异治疗效果的亚组。

DOI:
10.1002/sim.6454
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发表时间:
2015-05-20
影响因子:
2
通讯作者:
Man, Michael
Man, Michael
中科院分区:
医学3区
文献类型:
--
作者:
Loh, Wei-Yin;He, Xu;Man, Michael

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在与癌症等难以治疗的疾病的斗争中,往往很难发现对所有受试者都有益的新疗法。为了获得监管机构的批准,确定治疗效果增强的受试者亚组更为实际。回归树很适合这个任务,因为它们划分了数据空间。我们简要回顾了现有的回归树算法。然后,我们引入了三个新的,实际上没有选择偏差,适用于随机试验的数据,包括两种或更多的治疗,审查的响应变量,以及预测变量中的缺失值。这些算法通过使用三个关键思想扩展了GUIDE方法:(i)作为线性预测器的处理,(ii)卡方检验以检测剩余模式和缺乏拟合,以及(iii)通过泊松回归的比例风险建模。作为副产品,获得了具有识别影响变量阈值的重要性分数。采用自举技术为每个节点的处理效果构造置信区间。用实际数据和模拟数据对两种方法进行了比较。
In the fight against hard-to-treat diseases such as cancer, it is often difficult to discover new treatments that benefit all subjects. For regulatory agency approval, it is more practical to identify subgroups of subjects for whom the treatment has an enhanced effect. Regression trees are natural for this task because they partition the data space. We briefly review existing regression tree algorithms. Then we introduce three new ones that are practically free of selection bias and are applicable to data from randomized trials with two or more treatments, censored response variables, and missing values in the predictor variables. The algorithms extend the GUIDE approach by using three key ideas: (i) treatment as a linear predictor, (ii) chi-squared tests to detect residual patterns and lack of fit, and (iii) proportional hazards modeling via Poisson regression. Importance scores with thresholds for identifying influential variables are obtained as by-products. A bootstrap technique is used to construct confidence intervals for the treatment effects in each node. The methods are compared using real and simulated data.
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期刊: BIOMETRICS BULLETIN
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