title: Analysis of heart transplant survival data using generalized additive models.
abstract: The Stanford Heart Transplant data were collected to model survival in patients using penalized smoothing splines for covariates whose values change over the course of the study. The basic idea of the present study is to use a logistic regression model and a generalized additive model with B-splines to estimate the survival function. We model survival time as a function of patient covariates and transplant status and compare the results obtained using smoothing spline, partial logistic, Cox's proportional hazards, and piecewise exponential models.
authors.affiliation: Department of Engineering Informatics, Osaka Electro-Communication University, Osaka 572-8530, Japan. ekaaf900@ricv.zaq.ne.jp
authors.forename: Masaaki
authors.initials: M
authors.lastname: Tsujitani
authors.forename: Yusuke
authors.initials: Y
authors.lastname: Tanaka
mesh_terms: D000328:Adult; D000465:Algorithms; D019295:Computational Biology; D016027:Heart Transplantation / Q000401:mortality* / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008875:Middle Aged; D015233:Models, Statistical; D016016:Proportional Hazards Models; D016019:Survival Analysis