Pavlou, M;
Ambler, G;
Seaman, S;
De Iorio, M;
Omar, RZ;
(2016)
Review and evaluation of penalised regression methods for risk prediction in low-dimensional data with few events.
Statistics in Medicine
, 35
(7)
pp. 1159-1177.
10.1002/sim.6782.
Preview |
Text
Pavlou_et_al-Statistics_in_Medicine.pdf Download (495kB) | Preview |
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 likelihood estimation using real and simulated data. The simulation study showed that maximum likelihood estimation tends to produce overfitted models with poor predictive performance in scenarios with few events, and penalised methods can offer improvement. Ridge regression performed well, except in scenarios with many noise predictors. Lasso performed better than ridge in scenarios with many noise predictors and worse in the presence of correlated predictors. Elastic net, a hybrid of the two, performed well in all scenarios. Adaptive lasso and smoothly clipped absolute deviation performed best in scenarios with many noise predictors; in other scenarios, their performance was inferior to that of ridge and lasso. Bayesian approaches performed well when the hyperparameters for the priors were chosen carefully. Their use may aid variable selection, and they can be easily extended to clustered-data settings and to incorporate external information. © 2015 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd.
| Type: | Article |
|---|---|
| Title: | Review and evaluation of penalised regression methods for risk prediction in low-dimensional data with few events |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/sim.6782 |
| Publisher version: | http://dx.doi.org/10.1002/sim.6782 |
| Language: | English |
| Additional information: | © 2015 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Bayesian regularisation, overfitting, rare events, shrinkage |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1474006 |
Archive Staff Only
 |
View Item |
