Brief. Bioinformatics - Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data.

Tópicos

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Resumo

Developments in whole genome biotechnology have stimulated statistical focus on prediction methods. We review here methodology for classifying patients into survival risk groups and for using cross-validation to evaluate such classifications. Measures of discrimination for survival risk models include separation of survival curves, time-dependent ROC curves and Harrell's concordance index. For high-dimensional data applications, however, computing these measures as re-substitution statistics on the same data used for model development results in highly biased estimates. Most developments in methodology for survival risk modeling with high-dimensional data have utilized separate test data sets for model evaluation. Cross-validation has sometimes been used for optimization of tuning parameters. In many applications, however, the data available are too limited for effective division into training and test sets and consequently authors have often either reported re-substitution statistics or analyzed their data using binary classification methods in order to utilize familiar cross-validation. In this article we have tried to indicate how to utilize cross-validation for the evaluation of survival risk models; specifically how to compute cross-validated estimates of survival distributions for predicted risk groups and how to compute cross-validated time-dependent ROC curves. We have also discussed evaluation of the statistical significance of a survival risk model and evaluation of whether high-dimensional genomic data adds predictive accuracy to a model based on standard covariates alone.

Resumo Limpo

develop whole genom biotechnolog stimul statist focus predict method review methodolog classifi patient surviv risk group use crossvalid evalu classif measur discrimin surviv risk model includ separ surviv curv timedepend roc curv harrel concord index highdimension data applic howev comput measur resubstitut statist data use model develop result high bias estim develop methodolog surviv risk model highdimension data util separ test data set model evalu crossvalid sometim use optim tune paramet mani applic howev data avail limit effect divis train test set consequ author often either report resubstitut statist analyz data use binari classif method order util familiar crossvalid articl tri indic util crossvalid evalu surviv risk model specif comput crossvalid estim surviv distribut predict risk group comput crossvalid timedepend roc curv also discuss evalu statist signific surviv risk model evalu whether highdimension genom data add predict accuraci model base standard covari alon

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