#The commands below assume that the melanoma data set has been stored

# in the dataframe "melanoma" and that the survival library has been attached

# (cf. the R commands to practical exercise 3)

 

 

# Define sex as a categorical covariate

# (This is not really necessary for a categorical covariate with only two levels,

# but it is good practice to do so.)

melanoma$sex<-factor(melanoma$sex)

 

# Fit a univariate Cox regression model for gender and look at the results:

cox.mel<- coxph(Surv(lifetime,status==1)~sex, data=melanoma)

summary(cox.mel)

 

# Fit a univariate Cox regression model for tumor thickness and look at the results:

cox.mel<- coxph(Surv(lifetime,status==1)~ thickn, data=melanoma)

summary(cox.mel)

 

#It may be more appropriate to use the logartimhm (with base 2) of tumor thickness:

cox.mel<- coxph(Surv(lifetime,status==1)~ log(thickn,2), data=melanoma)

summary(cox.mel)

 

# Fit bivariate Cox regression with gender and log-thickness:

cox.mel<- coxph(Surv(lifetime,status==1)~sex+log(thickn,2), data=melanoma)

summary(cox.mel)