# Pt.1 create df1 - all mortality ratios over time
#======================================================
set.seed(5)
df1 <- data.frame(matrix(ncol=7,nrow=300))
colnames(df1) <- c("Trt","Day","PercentSurv","Status","V1","V2","V3")
df1$Trt <- rep(1:50, each=6)
df1$Day <- rep(c(0,2,4,6,8,10), times=50)
for(i in df1$Trt){
  df1$PercentSurv[df1$Trt %in% c(i)] <-
c("100",sort(sample(1:100,size=5),decreasing=TRUE))
  df1$V1[df1$Trt %in% c(i)] <- sample(12:30,size=1)
  df1$V2[df1$Trt %in% c(i)] <- sample(10:40,size=1)
  df1$V3[df1$Trt %in% c(i)] <- sample(1:5,size=1)
}
df1$PercentSurv <- as.numeric(df1$PercentSurv)
df1$Status[df1$PercentSurv > 15] <- as.numeric(1)
df1$Status[df1$PercentSurv <=15] <-as.numeric(2) '




# Pt.2 create df2 -  time-to-event with variables only
#==========================================================
df2 <- data.frame(matrix(ncol = 6,nrow=50))
colnames(df2) <- c("Trt", "Time", "Status","V1","V2","V3")
df2$Trt <- c(1:50)
for(i in df2$Trt){
  df2$Status[df2$Trt %in% c(i)] <- ifelse(  min(  df1$PercentSurv[df1$Trt
%in% c(i)]) <= 15,2,1)
  df2$Time[df2$Trt %in% c(i)] <- ifelse(df2$Status[df2$Trt %in%
c(i)]=="2",   df1$Day[df1$Trt %in% c(i)]
[min(which(df1$PercentSurv[df1$Trt %in% c(i)]<=15))],10)
  df2$V1[df2$Trt %in% c(i)] <- min(df1$V1[df1$Trt %in% c(i)])
  df2$V2[df2$Trt %in% c(i)] <- min(df1$V2[df1$Trt %in% c(i)])
  df2$V3[df2$Trt %in% c(i)] <- min(df1$V3[df1$Trt %in% c(i)])
  }
df2$Time <- as.numeric(df2$Time)


# Pt.3 use survival analysis on variables in df2  - cox test
#=========================================================

install.packages("survival")
install.packages("survminer")
library("survival")
library("survminer")

coxmodel <- coxph(Surv(Time, Status) ~ V1 + V2 + V3, data=df2)
summary(coxmodel)
ggsurvplot(survfit(coxmodel),data=df2, color = "#2E9FDF",
           ggtheme = theme_minimal())