Generalized additive models for medical research

T Hastie; R Tibshirani

doi:10.1177/096228029500400302

Generalized additive models for medical research

Stat Methods Med Res. 1995 Sep;4(3):187-96. doi: 10.1177/096228029500400302.

Authors

T Hastie¹, R Tibshirani

Affiliation

¹ Department of Statistics and Division of Biostatistics, Stanford University, California 94305, USA.

PMID: 8548102
DOI: 10.1177/096228029500400302

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Breast Neoplasms / drug therapy
Child, Preschool
Controlled Clinical Trials as Topic / statistics & numerical data*
Data Interpretation, Statistical
Disease-Free Survival
Female
Heart Defects, Congenital / surgery
Humans
Infant
Likelihood Functions
Logistic Models
Models, Statistical*
Prognosis
Proportional Hazards Models
Randomized Controlled Trials as Topic / statistics & numerical data
Risk Factors
Software
Statistics, Nonparametric*
Survival Analysis*