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
中科院分区:
文献类型:
--
作者:
Loh, Wei-Yin;He, Xu;Man, Michael
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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影响因子:
2
作者:
Foster, Jared C.;Taylor, Jeremy M. G.;Ruberg, Stephen J.
通讯作者:
Ruberg, Stephen J.
DOI:
10.1198/016214501753168271
发表时间:
2001-06-01
影响因子:
3.7
作者:
Kim, H;Loh, WY
通讯作者:
Loh, WY
影响因子:
2
作者:
Dusseldorp, Elise;Van Mechelen, Iven
通讯作者:
Van Mechelen, Iven
影响因子:
4.9
作者:
Mehta, Sunali;Shelling, Andrew;Print, Cristin
通讯作者:
Print, Cristin
DOI:
10.2307/3002019
发表时间:
1946-01-01
期刊:
BIOMETRICS BULLETIN
影响因子:
--
作者:
SATTERTHWAITE, FE
通讯作者:
SATTERTHWAITE, FE