Survival analysis in cancer using a partial logistic neural network model with Bayesian regularisation framework: a validation study
by Azzam F.G. Taktak, Antonio Eleuteri, Min S.H. Aung, Paulo J.G. Lisboa, Laurence Desjardins, Bertil E. Damato
International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP), Vol. 1, No. 3, 2009

Abstract: This paper describes a multicentre longitudinal cohort study to evaluate the predictive accuracy of a regularised Bayesian neural network model in a prognostic application. The study sample (n = 5442) comprises subjects treated with intraocular melanoma in two different centres in Liverpool and Paris. External validation was carried out by fitting the model to the data from Liverpool set and predicting for the data from Paris. The performance of the model in out-of-sample prediction was assessed statistically for discrimination of outcomes and calibration. It was also evaluated clinically by comparing against the accepted TNM staging system. The model had good discrimination with Harrell's C index > 0.7 up to ten years of follow-up. Calibration results were also good up to ten years using a Hosmer-Lemeshow type analysis (p > 0.05). The paper: 1) deals with the issue of missing data using methods that are well accepted in the literature; 2) proposes a framework for externally validating machine learning models applied to survival analysis; 3) applies accepted methods for dealing with missing data; 4) proposes an alternative staging system based on the model. The new staging system, which takes into account histopathologic information, has several advantages over the existing staging system.

Online publication date: Sat, 03-Oct-2009

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