Scientific article
English

Overview of model validation for survival regression model with competing risks using melanoma study data

Published inAnnals of Translational Medicine, vol. 6, no. 16, 325
Publication date2018
Abstract

The article introduces how to validate regression models in the analysis of competing risks. The prediction accuracy of competing risks regression models can be assessed by discrimination and calibration. The area under receiver operating characteristic curve (AUC) or Concordance-index, and calibration plots have been widely used as measures of discrimination and calibration, respectively. One-time splitting method can be used for randomly splitting original data into training and test datasets. However, this method reduces sample sizes of both training and testing datasets, and the results can be different by different splitting processes. Thus, the cross-validation method is more appealing. For time-to-event data, model validation is performed at each analysis time point. In this article, we review how to perform model validation using the riskRegression package in R, along with plotting a nomogram for competing risks regression models using the regplot() package.

Keywords
  • Calibration plot
  • Competing risk
  • Discrimination
  • Prediction model
Citation (ISO format)
ZHANG, Zhongheng et al. Overview of model validation for survival regression model with competing risks using melanoma study data. In: Annals of Translational Medicine, 2018, vol. 6, n° 16, p. 325. doi: 10.21037/atm.2018.07.38
Main files (1)
Article (Published version)
accessLevelRestricted
Identifiers
ISSN of the journal2305-5839
288views
0downloads

Technical informations

Creation10/11/2019 5:08:00 PM
First validation10/11/2019 5:08:00 PM
Update time03/15/2023 6:35:48 PM
Status update03/15/2023 6:35:47 PM
Last indexation10/31/2024 5:16:15 PM
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack