Summary
Objectives:
A lack of generally applicable tools for the assessment of predictions for survival
data has to be recognized. Prediction error curves based on the Brier score that have
been suggested as a sensible approach are illustrated by means of a case study.
Methods:
The concept of predictions made in terms of conditional survival probabilities given
the patient’s covariates is introduced. Such predictions are derived from various
statistical models for survival data including artificial neural networks. The idea
of how the prediction error of a prognostic classification scheme can be followed
over time is illustrated with the data of two studies on the prognosis of node positive
breast cancer patients, one of them serving as an independent test data set.
Results and Conclusions:
The Brier score as a function of time is shown to be a valuable tool for assessing
the predictive performance of prognostic classification schemes for survival data
incorporating censored observations. Comparison with the prediction based on the pooled
Kaplan Meier estimator yields a benchmark value for any classification scheme incorporating
patient’s covariate measurements. The problem of an overoptimistic assessment of prediction
error caused by data-driven modelling as it is, for example, done with artificial
neural nets can be circumvented by an assessment in an independent test data set.
Keywords
Prognostic models - survival data - Brier score - prediction error - validation -
breast cancer
