Gerds Thomas A, Schumacher Martin
Institute of Medical Biometry and Medical Informatics, University of Freiburg, Stefan-Meier-Strasse 26, D-79104 Freiburg, Germany.
Biometrics. 2007 Dec;63(4):1283-7. doi: 10.1111/j.1541-0420.2007.00832.x. Epub 2007 Jul 25.
Estimates of the prediction error play an important role in the development of statistical methods and models, and in their applications. We adapt the resampling tools of Efron and Tibshirani (1997, Journal of the American Statistical Association92, 548-560) to survival analysis with right-censored event times. We find that flexible rules, like artificial neural nets, classification and regression trees, or regression splines can be assessed, and compared to less flexible rules in the same data where they are developed. The methods are illustrated with data from a breast cancer trial.
预测误差的估计在统计方法和模型的发展及其应用中起着重要作用。我们将Efron和Tibshirani(1997年,《美国统计协会杂志》92卷,548 - 560页)的重采样工具应用于具有右删失事件时间的生存分析。我们发现,可以评估像人工神经网络、分类与回归树或回归样条这样的灵活规则,并在其构建所使用的相同数据中将它们与不太灵活的规则进行比较。通过一项乳腺癌试验的数据对这些方法进行了说明。