title: Continuous and discrete time survival analysis: neural network approaches.
abstract: In this paper we describe and compare two neural network models aimed at survival analysis modeling, based on formulations in continuous and discrete time. Learning in both models is approached in a Bayesian inference framework. We test the models on a real survival analysis problem, and we show that both models exhibit good discrimination and calibration capabilities. The C index of discrimination varied from 0.8 (SE=0.093) at year 1, to 0.75 (SE=0.034) at year 7 for the continuous time model; from 0.81 (SE=0.07) at year 1, to 0.75 (SE=0.033) at year 7 for the discrete time model. For both models the calibration was good (p<0.05) up to 7 years.
authors.affiliation: Department of Clinical Engineering, Royal Liverpool University Hospital, Liverpool, UK. antonio.eleuteri@gmail.com
authors.forename: A
authors.initials: A
authors.lastname: Eleuteri
authors.forename: M S H
authors.initials: MS
authors.lastname: Aung
authors.forename: A F G
authors.initials: AF
authors.lastname: Taktak
authors.forename: B
authors.initials: B
authors.lastname: Damato
authors.forename: P J G
authors.initials: PJ
authors.lastname: Lisboa
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D005134:Eye Neoplasms / Q000401:mortality*; D006801:Humans; D015994:Incidence; D008545:Melanoma / Q000401:mortality*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D016019:Survival Analysis; D015996:Survival Rate