Partial logistic artificial neural networks (PLANN) for flexible modeling of censored survival data

Linear and non-linear flexible regression analysis techniques, such as those based on splines and feed forward artificial neural networks (FFANN), have been proposed for the statistical analysis of censored survival time data, to account for the presence of non linear effects of predictors. Among survival functions, the hazard has a biological interest for the study of the disease dynamics, moreover it allows for the estimation of cumulative incidence functions for predicting outcome probabilities over follow-up. Therefore, specific error functions and data representation have been introduced for FFANN extensions of generalized linear models, in the perspective of modelling the hazard function of censored survival data. These techniques can be applied to account for the prognostic contribution of new biomarkers in addition to the traditional ones.