Non‐linear survival analysis using neural networks
- 11 February 2004
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 23 (5), 825-842
- https://doi.org/10.1002/sim.1655
Abstract
We describe models for survival analysis which are based on a multi-layer perceptron, a type of neural network. These relax the assumptions of the traditional regression models, while including them as particular cases. They allow non-linear predictors to be fitted implicitly and the effect of the covariates to vary over time. The flexibility is included in the model only when it is beneficial, as judged by cross-validation. Such models can be used to guide a search for extra regressors, by comparing their predictive accuracy with that of linear models. Most also allow the estimation of the hazard function, of which a great variety can be modelled. In this paper we describe seven different neural network survival models and illustrate their use by comparing their performance in predicting the time to relapse for breast cancer patients. Copyright © 2004 John Wiley & Sons, Ltd.Keywords
This publication has 7 references indexed in Scilit:
- Modern Applied Statistics with SStatistics and Computing, 2002
- Time distribution of the recurrence risk for breast cancer patients undergoing mastectomy: Further support about the concept of tumor dormancyBreast Cancer Research and Treatment, 1996
- Pattern Recognition and Neural NetworksPublished by Cambridge University Press (CUP) ,1996
- A neural network model for survival dataStatistics in Medicine, 1995
- Local Recurrences and Distant Metastases After Conservative Breast Cancer Treatments: Partly Independent EventsJNCI Journal of the National Cancer Institute, 1995
- Human breast cancer: prognostic significance of the c-erbB-2 oncoprotein compared with epidermal growth factor receptor, DNA ploidy, and conventional pathologic features.Journal of Clinical Oncology, 1992
- Generalized Linear ModelsPublished by Springer Science and Business Media LLC ,1989