library(survival)

#Surv() #create survival object
#survfit() # nonparametric estimates Kaplan-Meier, Fleming-Harington
           # estimate baseline hazard for coxph() object
#plot()  # method for survfit objects - survival curves and related objects
#coxph() # Cox model on survival times
#survreg() # parametric survival models 

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data(lung)
head(lung)
help(lung)
lung$st<-Surv(lung$time,lung$status)
lung$st[1:7]
sort(lung$st)[1:10] #sorted times
km<-survfit(st~1,data=lung) #K-M object
km
summary(km)

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###plots
plot(km) #simple K-M plot
plot(km, fun="event", ylim=c(0,1),ylab="Cumulative risk")
plot(km, fun="cumhaz",xlab="Time",ylab="Cumulative hazadr")
plot(survfit(st~sex,data=lung),main="Survival in males (blue) and females (red)", 
     xlab="Time since diagnosis (days)",
     ylab="Survival proportion", cex.axis=1.5, cex.lab=1.5, col=c("blue","red"))

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###Cox model

mas<-coxph(st~age+sex, data=lung)
summary(mas)

#prediction from Cox model: pred<-survfit(mod, newdata=....) mod is fitted coxph model, newdata specifies the covariate values

#proportionality of hazards
# cloglog = complementary log-log transform of S(t),  estimated log-cumulative hazadrs should be suffucuently parallel
plot (survfit(st~sex, data=lung),fun="cloglog",col=1:2,xlim=c(10,2000),xlab="Time(days)",
      main="Cum.hazard in men (blue) and women(red)") # !violation against the proporcionality
m1<-coxph(st~sex+ph.ecog+pat.karno, data=lung)
m1
cox.zph(m1) # testing assumption of proporcionality by chcking, wheather the corresponding parameters (& hazard ratios) may vary in time

# startified model 
# if the covariate is not an exposure of interest , but needs to be adjusted -> fit stratified model
m2<-coxph(st~strata(sex)+ph.ecog+pat.karno, data=lung)
m2
# if the covariate is a factor of interest, one may consider transformations of it - or completely different model - NON-PROPORTIONAL!