title: Analysis of survival data having time-dependent covariates.
abstract: Cox's proportional hazards model has been widely used for the analysis of treatment and prognostic effects with censored survival data. In this paper, we propose a neural network model based on bootstrapping to estimate the survival function and predict the short-term survival at any time during the course of the disease. The bootstrapping for the neural network is introduced when selecting the optimum number of hidden units and testing the goodness-of-fit. The proposed methods are illustrated using data from a long-term study of patients with primary biliary cirrhosis (PBC).
authors.affiliation: Department of Engineering Informatics, Osaka Electro-Communication University, Osaka 658-0032, Japan. ekaaf900@ricv.zaq.ne.jp
authors.forename: Masaaki
authors.initials: M
authors.lastname: Tsujitani
authors.forename: Masato
authors.initials: M
authors.lastname: Sakon
mesh_terms: D000328:Adult; D000367:Age Factors; D000465:Algorithms; D001663:Bilirubin / Q000032:analysis; D018450:Disease Progression; D006801:Humans; D008105:Liver Cirrhosis, Biliary / Q000401:mortality* / Q000503:physiopathology; D016015:Logistic Models; D008875:Middle Aged; D009026:Mortality; D016571:Neural Networks, Computer; D011379:Prognosis; D016016:Proportional Hazards Models; D011517:Prothrombin Time; D016019:Survival Analysis; D013997:Time Factors
chemical_list: D001663:Bilirubin