Goal: To find all critical variables that influence time to death so as to not record unneeded variables in future observations.
Call: coxph(formula = Surv(time, DEATH_EVENT) ~ age + anaemia + creatinine_phosphokinase + ejection_fraction + serum_creatinine + serum_sodium + hypertension, data = HF) coef exp(coef) se(coef) z p age 4.357e-02 1.045e+00 8.831e-03 4.934 8.05e-07 anaemia1 4.460e-01 1.562e+00 2.150e-01 2.074 0.0380 creatinine_phosphokinase 2.101e-04 1.000e+00 9.825e-05 2.138 0.0325 ejection_fraction -4.747e-02 9.536e-01 1.027e-02 -4.621 3.82e-06 serum_creatinine 3.139e-01 1.369e+00 6.895e-02 4.552 5.31e-06 serum_sodium -4.569e-02 9.553e-01 2.336e-02 -1.956 0.0505 hypertensionPresent 4.965e-01 1.643e+00 2.137e-01 2.324 0.0201 Likelihood ratio test=80.58 on 7 df, p=1.048e-14 n= 299, number of events= 96
This above selection of variables was derived from performing backward stepwise selection. 3 other variables were eliminated.
If you look at
serum_sodium, you'll see a p-value of 0.0505 which is just over alpha = 0.05. I've never run into a situation in which the p-value is that close to alpha.
What steps are usually taken to decide on whether to include it or not?
I performed a likelihood ratio test between the models and the null hypothesis, both models perform equally well, was not rejected. In the end, I dropped the variable but would appreciate responses had I not chosen this route.