A BOOTSTRAP RESAMPLING PROCEDURE FOR MODEL-BUILDING - APPLICATION TO THE COX REGRESSION-MODEL
A BOOTSTRAP RESAMPLING PROCEDURE FOR MODEL-BUILDING - APPLICATION TO THE COX REGRESSION-MODEL
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DOI：
10.1002/sim.4780111607
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发表时间：
1992-12-01
影响因子：
2
通讯作者：
SCHUMACHER, M
中科院分区：
文献类型：
--
作者：
SAUERBREI, W;SCHUMACHER, M
在临床研究的统计分析中，一个常见的问题是在回归模型的框架中选择那些可能影响结果变量的变量。逐步方法已经存在很长时间了，但与许多其他可能的策略一样，对它们的使用有很多批评。通常需要对选定模型的稳定性进行调查，但通常没有以系统的方式进行。由于分析方法极其困难，依赖数据的方法可能是一个有用的替代方法。Chen和乔治基于Bootstrap回归程序，在考克斯比例风险回归模型的框架下研究了逐步选择程序的稳定性。我们扩展了他们的建议，并开发了一个bootstrap模型的选择过程，结合bootstrap方法与现有的选择技术，如逐步的方法。我们使用两个癌症临床试验的数据，具有两种不同的情况下，在临床研究中常见的模型构建过程中说明了所提出的策略。在一项脑肿瘤研究中，总体治疗比较中协变量的调整是主要关注点，需要选择甚至是“轻度”效应。在前列腺癌研究中，我们集中分析治疗协变量相互作用，要求只选择“强”效应。该策略的两种变体都将通过使用考克斯模型分析临床试验来证明，但它们可以通过明显和直接的修改应用于其他类型的回归。
A common problem in the statistical analysis of clinical studies is the selection of those variables in the framework of a regression model which might influence the outcome variable. Stepwise methods have been available for a long time, but as with many other possible strategies, there is a lot of criticism of their use. Investigations of the stability of a selected model are often called for, but usually are not carried out in a systematic way. Since analytical approaches are extremely difficult, data-dependent methods might be an useful alternative. Based on a bootstrap resampling procedure, Chen and George investigated the stability of a stepwise selection procedure in the framework of the Cox proportional hazard regression model. We extend their proposal and develop a bootstrap-model selection procedure, combining the bootstrap method with existing selection techniques such as stepwise methods. We illustrate the proposed strategy in the process of model building by using data from two cancer clinical trials featuring two different situations commonly arising in clinical research. In a brain tumour study the adjustment for covariates in an overall treatment comparison is of primary interest calling for the selection of even 'mild' effects. In a prostate cancer study we concentrate on the analysis of treatment covariate interactions demanding that only 'strong' effects should be selected. Both variants of the strategy will be demonstrated analysing the clinical trials with a Cox model, but they can be applied in other types of regression with obvious and straightforward modifications.