Comparing measures of model selection for penalized splines in Cox models

Authors:
Elizabeth J. Malloy;Donna Spiegelman;Ellen A. Eisen
Affiliations:
Department of Mathematics and Statistics, American University, Washington, DC 20016, USA;Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA and Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA;Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115, USA and School of Public Health, University of California, Berkeley, CA 94704, USA
Venue:
Computational Statistics & Data Analysis
Year:
2009

Citing 4
Cited 1

Nonparametric estimation of relative risk using splines and cross-validation

SIAM Journal on Scientific and Statistical Computing
Smoothing parameter selection for smoothing splines: a simulation study

Computational Statistics & Data Analysis
Normalization of two-channel microarray experiments: a semiparametric approach

Bioinformatics
Estimating the functional form of a continuous covariate's effect on survival time

Computational Statistics & Data Analysis

A comparative study of nonparametric estimation in Weibull regression: A penalized likelihood approach

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

This article presents an application and a simulation study of model fit criteria for selecting the optimal degree of smoothness for penalized splines in Cox models. The criteria considered were the Akaike information criterion, the corrected AIC, two formulations of the Bayesian information criterion, and a generalized cross-validation method. The estimated curves selected by the five methods were compared to each other in a study of rectal cancer mortality in autoworkers. In the stimulation study, we estimated the fit of the penalized spline models in six exposure-response scenarios, using the five model fit criteria. The methods were compared on the basis of a mean squared error score and the power and size of hypothesis tests for any effect and for detecting nonlinearity. All comparisons were made across a range in the total sample size and number of cases.