Table 4.

Predictive results for the high-dimensional data (p = 4000): We used the proposed method, denoted by (A), Bayesian Cox method, denoted by (B), and Cox lasso method, denoted by (C).

			$\left(\mathrm{A}\right)$			$\left(\mathrm{B}\right)$			$\left(\mathrm{C}\right)$
n	p	$\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{o}\mathrm{r}$	AUC	PRC	CCI	AUC	PRC	CCI	AUC	PRC	CCI
1000	4000	$20\mathrm{\%}$	0.838	0.831	0.827	0.811	0.802	0.811	0.837	0.833	0.825
			(0.012)	(0.016)	(0.009)	(0.014)	(0.019)	(0.010)	(0.011)	(0.015)	(0.008)
		$30\mathrm{\%}$	0.837	0.829	0.828	0.817	0.807	0.815	0.837	0.832	0.824
			(0.011)	(0.016)	(0.008)	(0.012)	(0.017)	(0.008)	(0.012)	(0.016)	(0.008)
		$40\mathrm{\%}$	0.838	0.831	0.828	0.824	0.815	0.817	0.837	0.831	0.825
			(0.011)	(0.014)	(0.008)	(0.012)	(0.016)	(0.010)	(0.013)	(0.016)	(0.009)
3000	4000	$20\mathrm{\%}$	0.838	0.833	0.829	0.803	0.790	0.832	0.837	0.834	0.825
			(0.006)	(0.008)	(0.004)	(0.006)	(0.009)	(0.005)	(0.006)	(0.008)	(0.005)
		$30\mathrm{\%}$	0.838	0.832	0.828	0.817	0.805	0.832	0.835	0.832	0.824
			(0.006)	(0.007)	(0.004)	(0.007)	(0.009)	(0.004)	(0.006)	(0.008)	(0.004)
		$40\mathrm{\%}$	0.838	0.833	0.828	0.827	0.817	0.832	0.837	0.833	0.825
			(0.006)	(0.008)	(0.004)	(0.006)	(0.010)	(0.004)	(0.005)	(0.008)	(0.004)

Note: Performance was assessed via three measures abbreviated as AUC (area under the receiver operating characteristic curve), PRC (area under the precision-recall curve), and CCI (concordance index). n is the sample size, p is the dimension of the covariates, and censor denotes the censoring rate, where the standard deviations are provided in parentheses.