Table 4 Regression coefficients and P-values for various parametric survival Models.

From: A comparative analysis of parametric survival models and machine learning methods in breast cancer prognosis

Variables

Weibull

Exponential

Gaussian

Logistic

Log-Logistic (e-05)

Log-Gaussian

Age

0.01044 (p < 2e-16)

0.0026 (p = 0.94)

0.666 (p = 0.044)

0.2973 (p = 0.17)

4.60e-03 (p = 0.27)

0.020128 (p = 0.035)

Grade

−0.03753 (p < 2e-16)

0.0112 (p = 0.63)

−0.1646 (p = 0.413)

−0.1180 (p = 0.37)

−1.84e-03 (p = 0.47)

−0.002423 (p = 0.67)

Primary Site

−0.01319 (p < 2e-16)

0.0088 (p = 0.44)

0.0276 (p = 0.78)

−0.0017 (p = 0.97)

4.48e-05 (p = 0.97)

0.001903 (p = 0.50)

Marital Status

−0.04215 (p < 2e-16)

0.0437 (p = 0.11)

0.2864 (p = 0.220)

0.1868 (p = 0.22)

3.08e-03 (p = 0.29)

0.011280 (p = 0.09)

AJCC Stage

−0.17032 (p < 2e-16)

0.0918 (p = 0.04)

−0.0287 (p = 0.94)

−0.1452 (p = 0.57)

−1.58e-03 (p = 0.75)

0.000678 (p = 0.953)

Race

−0.00646 (p < 2e-16)

−0.0149 (p = 0.71)

−0.6764 (p = 0.052)

−0.2703 (p = 0.24)

−4.71e-03 (p = 0.29)

−0.018576 (p = 0.06)

Radiation

−0.03558 (p < 2e-16)

0.0032 (p = 0.75)

−0.3894 (p = 6.3e-06)

−0.1625 (p = 0.004)

−2.77e-03 (p = 0.01)

−0.010289 (p = 3.6e-05)

Chemotherapy

0.02296 (p < 2e-16)

−0.0093 (p = 0.86)

0.0135 (p = 0.977)

0.1238 (p = 0.69)

1.70e-03 (p = 0.77)

−0.002486 (p = 0.856)