Continuous and discrete time survival analysis: neural network approaches.
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.
Eleuteri
A
A
Department of Clinical Engineering, Royal Liverpool University Hospital, Liverpool, UK. antonio.eleuteri@gmail.com
Aung
M S H
MS
Taktak
A F G
AF
Damato
B
B
Lisboa
P J G
PJ
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