Generalized additive models for medical research.
This article reviews flexible statistical methods that are useful for characterizing the effect of potential prognostic factors on disease endpoints. Applications to survival models and binary outcome models are illustrated.
Hastie
T
T
Department of Statistics and Division of Biostatistics, Stanford University, California 94305, USA.
Tibshirani
R
R
D000465:Algorithms; D001943:Breast Neoplasms / Q000188:drug therapy; D002675:Child, Preschool; D018849:Controlled Clinical Trials as Topic / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D018572:Disease-Free Survival; D005260:Female; D006330:Heart Defects, Congenital / Q000601:surgery; D006801:Humans; D007223:Infant; D016013:Likelihood Functions; D016015:Logistic Models; D015233:Models, Statistical; D011379:Prognosis; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012307:Risk Factors; D012984:Software; D018709:Statistics, Nonparametric; D016019:Survival Analysis