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Table 4 Average number of non-zero coefficients and mean absolute error (MAE) of coefficient estimates over 100 simulations for scenario 1 and 2

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

  Method Average
Number
MAE
Scenario 1 gsslasso 8.61 0.60 (0.24)
lasso 51.99 3.77 (0.40)
grlasso 474.80 12.43 (1.64)
grMCP 62.00 9.30 (2.56)
grSCAD 108.80 8.41 (1.25)
cMCP 14.19 0.96 (0.34)
SGL 39.79 6.25 (1.65)
Scenario 2 gsslasso 9.74 1.29 (0.84)
lasso 53.70 4.02 (0.46)
grlasso 502.05 12.11 (2.08)
grMCP 57.13 8.04 (0.67)
grSCAD 167.59 8.77 (0.93)
cMCP 15.14 0.96 (0.33)
  1. *: the optimal s0 = 0.02 and s0 = 0.03 for gsslasso method 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