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 …

BACKGROUND There is a great need for accurate treatment and outcome prediction in
cancer. Two methods for prediction, artificial neural networks and Kaplan–Meier plots, have …

Hupertan et al. 1 have done a nice analysis of the accuracy of a previously published
nomogram2 when applied to an external dataset. The authors used a sample of 565 men …

The holygrail of prognosis research is to improve patient outcomes by enabling a more
personalized (rather than population-or group-based) approach to healthcare and risk …

Cancer prediction models are becoming ubiquitous, yet we generally have no idea whether
they do more good than harm. This is because current statistical methods for evaluating …

Multivariable prognostic models are widely used in cancer and other disease areas, and
have a range of applications in clinical medicine, clinical trials and allocation of health …

Prognostic factor studies in cancer relate covariates describing clinical features and tumor
characteristics to clinical endpoints such as therapy response, disease recurrence or …

Background Appropriate choice and use of prognostic models in clinical practice require the
use of good methods for both model development, and for developing prognostic indices …

This book is organized into 4 sections, each looking at the question of outcome prediction in
cancer from a different angle. The first section describes the clinical problem and some of …

This paper provides a systematic comparison of cancer mortality and incidence projection
methods used at major national health agencies. These methods include Poisson …