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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
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BMC Bioinformatics

ISSN: 1471-2105

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