Review and evaluation of penalised regression methods for risk prediction in low-dimensional data with few events.

ABSTRACT: Risk prediction models are used to predict a clinical outcome for patients using a set of predictors. We focus on predicting low-dimensional binary outcomes typically arising in epidemiology, health services and public health research where logistic regression is commonly used. When the number of events is small compared with the number of regression coefficients, model overfitting can be a serious problem. An overfitted model tends to demonstrate poor predictive accuracy when applied to new data. We review frequentist and Bayesian shrinkage methods that may alleviate overfitting by shrinking the regression coefficients towards zero (some methods can also provide more parsimonious models by omitting some predictors). We evaluated their predictive performance in comparison with maximum like

SUBMITTER: Pavlou M 

PROVIDER: S-EPMC4982098 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature