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Efron‐Type Measures of Prediction Error for Survival Analysis
Author(s) -
Gerds Thomas A.,
Schumacher Martin
Publication year - 2007
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2007.00832.x
Subject(s) - resampling , computer science , regression , statistics , type i and type ii errors , regression analysis , survival analysis , machine learning , event (particle physics) , covariate , artificial intelligence , data mining , econometrics , mathematics , physics , quantum mechanics
Summary 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 Association 92, 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.