Bootstrap assessment of the stability of multivariable models
Bootstrap assessment of the stability of multivariable models
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DOI：
10.1177/1536867x0900900403
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发表时间：
2009-01-01
期刊：
影响因子：
4.8
通讯作者：
Sauerbrei, Willi
中科院分区：
文献类型：
--
作者：
Royston, Patrick;Sauerbrei, Willi
评估多变量模型的不稳定性很重要，但在实践中很少这样做。当选定的预测变量（对于多变量分数多项式建模，选定的连续预测变量函数）对数据的微小变化敏感时，会发生模型不稳定。Bootstrap分析是一种有用的技术，用于调查随机抽取的替换样本中所选模型之间的差异。这些样本模拟的数据集在结构上与正在研究的数据集相似，并且可能会出现替代。候选变量的自举包含分数有效地指示变量的重要性。我们描述了Stata工具的稳定性分析的上下文中，多变量模型构建的mfp命令。我们提供了实用的指导，并说明了工具在前列腺癌研究中的应用。
Assessing the instability of a multivariable model is important but is rarely done in practice. Model instability occurs when selected predictors-and for multivariable fractional polynomial modeling, selected functions of continuous predictors-are sensitive to small changes in the data. Bootstrap analysis is a useful technique for investigating variations among selected models in samples drawn at random with replacement. Such samples mimic datasets that are structurally similar to that under study and that could plausibly have arisen instead. The bootstrap inclusion fraction of a candidate variable usefully indicates the importance of the variable. We describe Stata tools for stability analysis in the context, of the mfp command for multivariable model building. We offer practical guidance and illustrate the application of the tools to a study in prostate cancer.