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Table 6 The measures of optimal group spike-and-slab lasso (gsslasso) cox and the lasso cox models for TCGA ovarian cancer, lung adenocarcinoma (LUAD) and breast cancer dataset with pathway genes by 10 times 10-fold cross validation

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

  Pathway
number
Genes
included
Methods CVPL C-index Number of
non-zero gene
TCGA 271 4260 gsslasso − 1041.218 (2.118) 0.577 (0.012) 33
ovarian lasso − 1042.905 (1.687) 0.533 (0.027) 15
cancer grlasso − 1044.110 (12.741) 0.504 (0.014) 24
N = 304 grMCP − 1046.965 (8.604) 0.502 (0.007) 24
grSCAD −1042.349 (5.339) 0.503 (0.012) 24
cMCP − 1043.373 (2.215) 0.532 (0.019) 13
TCGA 274 4266 gsslasso − 938.973 (1.675) 0.559 (0.010) 64
LUAD lasso − 941.383 (3.720) 0.545 (0.019) 13
N = 491 grlasso − 945.605 (8.137) 0.547 (0.023) 111
grMCP − 1092.091 (30.477) 0.512 (0.015) 25
grSCAD − 940.358 (1.331) 0.538 (0.021) 123
cMCP −942.831 (3.301) 0.530 (0.022) 3
TCGA 275 4385 gsslasso −996.491 (2.131) 0.640 (0.153) 86
Breast lasso −1002.046 (5.356) 0.523 (0.027) 2
cancer grlasso − 1001.073 (9.641) 0.590 (0.022) 93
N = 1082 grMCP − 1016.864 (25.290) 0.520 (0.019) 12
grSCAD − 1005.299 (2.268) 0.522 (0.007) 24
cMCP − 1012.587 (44.339) 0.502 (0.012) 1
  1. Note: Values in the parentheses are standard errors. For group spike-and-slab lasso model, the optimal s0 = 0.03 for three data sets. In TCGA ovarian cancer, we mapped 4260 genes into 271 pathways. The analyses was performed on these genes including in these pathways. The same is true for other two datasets