Related Experiment Video
Updated: Aug 11, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
On the problem of using 'optimal' cutpoints in the assessment of quantitative prognostic factors
1Institut für Medizinische Biometrie und Medizinische Informatik, Universitätsklinikum Freiburg, Freiburg. noh@fdm.uni-freiburg.de
Abstract:
The identification and assessment of prognostic factors is an important task in clinical cancer research. Quantitative prognostic factors are often categorized by using one or several cutpoints to obtain an easier interpretation with respect to prognosis of the resulting patients' subgroups. Considering the selection of a data-driven 'optimal' cutpoint as a 'prototype' of statistical model building, we demonstrate that prognostic relevance of a single factor with no effect can solely be produced by the statistical model building process. Furthermore, we show how to overcome these problems by using corrected P-values and shrinkage methods. The problem and its solutions are illustrated by using the data of two breast cancer studies.
Related Concept Videos
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Receiver Operating Characteristic Plot
Kaplan-Meier Approach
Comparing the Survival Analysis of Two or More Groups
The Mantel-Cox Log-Rank Test
Cancer Survival Analysis

