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Table 3 Estimates of two measures over 100 replicates for varying group size and varying number of non-null group under simulation scenario 3,4 and 5, respectively

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

scenario 3

scenario 4

scenario 5

Group size

methods

CVPL

C-index

number of

non-null

group

CVPL

C-index

Correlation

coefficients

within group

CVPL

C-index

4/4

gsslasso

− 1130.995 (58.229)

0.829 (0.0513)

8/20

− 1090.819 (53.224)

0.875 (0.010)

r = 0.0

−1077.130 (57.084)

0.876 (0.009)

lasso

−1167.319 (57.844)

0.813 (0.015)

− 1113.349 (52.438)

0.870 (0.010)

− 1104.924 (56.431)

0.870 (0.010)

grlasso

− 1137.892 (57.414)

0.827 (0.014)

− 1266.185 (57.782)

0.746 (0.018)

− 1174.234 (57.919)

0.829 (0.014)

grMCP

− 1131.451 (57.960)

0.829 (0.013)

− 1334.359 (58.901)

0.616 (0.029)

− 1287.124 (64.897)

0.747 (0.035)

grSCAD

− 1132.272 (58.315)

0.829 (0.013)

− 1305.299 (58.587)

0.721 (0.025)

− 1258.988 (62.970)

0.795 (0.026)

cMCP

−1131.483 (58.339)

0.829 (0.013)

− 1094.230 (52.983)

0.875 (0.010)

−1082.770 (57.483)

0.875 (0.010)

4/20

gsslasso

− 1149.792 (56.801)

0.830 (0.013)

3/20

− 1120.043 (62.936)

0.849 (0.013)

r = 0.5

− 1087.823 (56.773)

0.865 (0.011)

lasso

− 1179.653 (56.986)

0.813 (0.014)

−1149.463 (61.507)

0.836(0.015)

− 1119.388 (56.076)

0.852 (0.013)

grlasso

−1179.498 (55.463)

0.811 (0.013)

− 1213.466 (62.431)

0.784 (0.018)

− 1157.999 (54.642)

0.828 (0.013)

grMCP

− 1172.856 (56.712)

0.816 (0.013)

− 1318.758 (64.886)

0.685 (0.033)

− 1226.349 (62.257)

0.778 (0.018)

grSCAD

−1172.884 (56.852)

0.816 (0.013)

− 1278.753 (62.726)

0.756 (0.023)

− 1208.197 (63.032)

0.801 (0.018)

cMCP

− 1150.915 (56.806)

0.827 (0.013)

− 1122.606 (62.852)

0.848(0.014)

− 1089.138 (56.817)

0.864 (0.011)

4/50

gsslasso

− 1145.155 (56.523)

0.825 (0.013)

1/20

− 1141.219 (60.329)

0.824 (0.014)

r = 0.7

−1113.142 (60.749)

0.852 (0.012)

lasso

− 1176.796 (56.449)

0.810 (0.015)

−1172.768 (57.418)

0.810 (0.014)

−1130.099 (60.286)

0.834 (0.013)

grlasso

−1208.999 (55.893)

0.782 (0.017)

− 1180.395 (58.095)

0.802 (0.017)

− 1164.874 (59.066)

0.814 (0.013)

grMCP

− 1272.423 (73.279)

0.782 (0.082)

− 1178.416 (64.849)

0.808 (0.016)

− 1202.094 (62.653)

0.822 (0.013)

grSCAD

− 1271.286 (58.185)

0.777 (0.018)

−1178.827 (65.082)

0.808 (0.016)

− 1195.481 (63.401)

0.852 (0.012)

cMCP

− 1148.318 (56.896)

0.824 (0.013)

− 1147.845 (59.271)

0.821 (0.014)

− 1117.158 (61.869)

0.858 (0.013)

  1. Notes: in scenario 3, group size “4/50” denotes that there are four none-zero coefficients embedded in a group with 50 predictors. The group size is 50. This is true for “4/20” and “4/4”. The optimal s0 = 0.02 for different group size settings. In scenario 4, “8/20” denotes that there are 8 non-null groups among 20 groups. Each non-null group includes at least one non-zero coefficients. The optimal s0 = 0.02 for the three settings. In scenario 5, the optimal s0 are 0.02, 0.03, and 0.04 for different correlation coefficients, 0.0, 0.5, and 0.7 within group, respectively. The slab scales, s1, are 1 in this scenario 3 4, and 5. Values in the parentheses are standard errors. “gsslasso” represents the proposed group spike-and-slab lasso cox
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BMC Bioinformatics

ISSN: 1471-2105

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