Bootstrap methods for developing predictive models
Bootstrap methods for developing predictive models
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DOI：
10.1198/0003130043277
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发表时间：
2004-05-01
影响因子：
1.8
通讯作者：
Tu, JV
中科院分区：
文献类型：
--
作者：
Austin, PC;Tu, JV
研究人员经常使用自动化模型选择方法，如向后消除，以确定变量是所考虑的结果的独立预测因素。我们建议使用自举响应结合自动变量选择方法来开发简约的预测模型。使用心脏病发作住院患者的数据，我们证明，选择那些在至少60%的自助样本中被确定为死亡率独立预测因子的变量，可以产生一个具有良好预测能力的简约模型。
Researchers frequently use automated model selection methods such as backwards elimination to identify variables that are independent predictors of an outcome under consideration. We propose using bootstrap resampling in conjunction with automated variable selection methods to develop parsimonious prediction models. Using data on patients admitted to hospital with a heart attack, we demonstrate that selecting those variables that were identified as independent predictors of mortality in at least 60% of the bootstrap samples resulted in a parsimonious model with excellent predictive ability.