# This file from www.richardsconsulting.co.uk/lectures

# First load in the survival library
library(survival)
Loading required package: splines

# Now create a list of (i) survival times, (ii) censoring/event status,
# and (iii) genders
#
# Note that '1' = death and '0' = censored in the status variable, and that
#           '0' = male and '1' = female in the gender variable.
example2<-list(time  =c(2, 5, 9, 11, 14, 4, 7, 10, 12, 15),
               status=c(1, 0, 1,  0,  1, 1, 0,  0,  0,  1),
               gender=c(0, 0, 0,  0,  0, 1, 1,  1,  1,  1))

# Fit a Cox proportional-hazards model
coxfit<-coxph(Surv(time, status) ~ gender, example2)

# Take a look at the value for the z test
beta<- coxfit$coef
stderr<-sqrt(coxfit$var)
z<-beta / stderr
z*z
1.001630

# Now the score test:
coxfit$score
1.117647

# And finally the LRT:
-2*(coxfit$loglik[1] - coxfit$loglik[2])
1.163364
 
# Compare these values with the output given in the summary() command:
summary(coxfit)
Call:
coxph(formula = Surv(time, status) ~ gender, data = example2)

  n= 10 
        coef exp(coef) se(coef)  z    p
gender -1.16     0.315     1.16 -1 0.32

       exp(coef) exp(-coef) lower .95 upper .95
gender     0.315       3.18    0.0326      3.03

Rsquare= 0.11   (max possible= 0.753 )
Likelihood ratio test= 1.16  on 1 df,   p=0.281
Wald test            = 1  on 1 df,   p=0.317
Score (logrank) test = 1.12  on 1 df,   p=0.290