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Pavlou, M., Ambler, G., Seaman, S., De Iorio, M. and Omar, R.Z. (2016) Review and Evaluation of Penalised Regression Methods for Risk Prediction in Low-Dimensional Data with Few Events. Statistics in Medicine, 35, 1159-1177.
https://doi.org/10.1002/sim.6782

has been cited by the following article:

• TITLE: Evaluating Common Strategies for the Efficiency of Feature Selection in the Context of Microarray Analysis

  AUTHORS: Melania Pintilie, Jenna Sykes

  KEYWORDS: Elastic Net, MARSA, LASSO, Feature Selection

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.5 No.1, February 9, 2017

  ABSTRACT: The recent explosion of high-throughput technology has been accompanied by a corresponding rapid increase in the number of new statistical methods for developing prognostic and predictive signatures. Three commonly used feature selection techniques for time-to-event data: single gene testing (SGT), Elastic net and the Maximizing R Square Algorithm (MARSA) are evaluated on simulated datasets that vary in the sample size, the number of features and the correlation between features. The results of each method are summarized by reporting the sensitivity and the Area Under the Receiver Operating Characteristic Curve (AUC). The performance of each of these algorithms depends heavily on the sample size while the number of features entered in the analysis has a much more modest impact. The coefficients estimated utilizing SGT are biased towards the null when the genes are uncorrelated and away from the null when the genes are correlated. The Elastic Net algorithms perform better than MARSA and almost as well as the SGT when the features are correlated and about the same as MARSA when the features are uncorrelated.