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Table 2 Estimates of two measures over 100 replicates under simulation scenario 1 and 2

From: Gsslasso Cox: a Bayesian hierarchical model for predicting survival and detecting associated genes by incorporating pathway information

  Methods CVPL C-index
Scenario 1 gsslasso − 1111.541(52.390) 0.848(0.012)
lasso − 1140.742(52.108) 0.836(0.013)
grplasso − 1198.449(53.664) 0.792(0.017)
grMCP − 1280.783(66.870) 0.736(0.039)
grSCAD − 1256.297(57.293) 0.752(0.027)
cMCP − 1114.934(53.278) 0.847(0.012)
SGL − 1167.902(72.121) 0.826(0.016)
Scenario 2 gsslasso − 1077.398(56.949) 0.868(0.011)
lasso −1114.886(56.200) 0.853(0.012)
grplasso − 1161.058 (59.318) 0.825 (0.015)
grMCP − 1236.072(67.840) 0.775(0.018)
grSCAD − 1219.129(66.240) 0.798(0.020)
cMCP − 1078.363 (57.004) 0.866 (0.011)
  1. Note: Values in the parentheses are standard deviations. “gsslasso” represents the proposed group spike-and-slab lasso cox. The slab scales, s1, are 1 in the analyses. The optimal s0 = 0.02 and s0 = 0.03 for gsslasso cox methods under scenario 1 and 2, respectively. For scenarios with overlap structures, SGL method was not used for comparison since it cannot handle overlap situation directly