Table 1.

Comparison of methods in simulated data.

	LASSO	CPSS	SBWR
ROC AUCs
119 covariates, ‘full size’ effects	0.92 (0.04)	0.99 (0.01)	0.99 (0.01)
119 covariates, ‘half size’ effects	0.86 (0.06)	0.93 (0.05)	0.97 (0.03)
10,000 covariates, ‘full size’ effects	0.99 (0.00)	1.00 (0.00)	1.00 (0.00)
10,000 covariates, ‘half size’ effects	0.95 (0.05)	0.95 (0.03)	0.99 (0.01)
20,000 covariates, ‘full size’ effects	0.92 (0.02)	0.95 (0.02)	0.96 (0.04)
20,000 covariates, ‘half size’ effects	0.72 (0.05)	0.78 (0.03)	0.82 (0.15)
Selection rates under the null
119 covariates	0.60 (0.49)	0.48 (0.32)	0.14 (0.25)
10,000 covariates	9.2E – 4 (0.03)	6.9E – 4 (5.0E-3)	1.6E – 6 (2.1E – 5)
20,000 covariates	0 (0)	1.8E – 4 (1.8E-3)	0 (0)

CPSS: Complementary Pairs Stability Selection; SBWR: Sparse Bayesian Weibull Regression

The top part of the table presents areas under the receiver operator characteristic curve (ROC AUCs) for detection of the 12 true effects among the variables analysed. Results are averaged over the analysis of 20 replicate datasets for each simulation scenario, with the standard deviation across replicates included in brackets. The bottom part of the table presents mean selection rates of each method under the null, over all covariates and all simulation replicates, with the standard deviation included in brackets.