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library(readxl)
D1 <- read_excel("D:\\Academic\\4H\\402\\Data-1.xlsx")

library(survival)
km.model <- survfit(Surv(Time,Died) ~ 1,
                    data = D1,
                    type = "kaplan-meier")
km.model
summary(km.model)

plot(km.model)
plot(km.model, conf.int = T, xlab = "Time(month)", ylab = "%Alive",
     main = "kM-Model", las =1, mark.time = T)

km.model2 <- survfit(Surv(Time, Died) ~ Over_40,
                     data = D1,
                     type = "kaplan-meier")
km.model2
summary(km.model2)

plot(km.model2)
plot(km.model2, conf.int = F, xlab = "Time(month)", ylab = "%Alive",
     main = "kM-Model", las = 1, mark.time = T)

survdiff(Surv(Time, Died) ~ Over_40,
         data = D1)

#2


library(readr)
D2 = read_csv("D:\\Academic\\4H\\402\\Data-2.csv")

D2$censored[D2$Turnover == 1] <- 1
D2$censored[D2$Turnover == 0 | D2$Turnover == 2] <- 0

library(survival)
km.fit1 <- survfit(Surv(LOS, censored)~1,
                   data = D2,
                   type = "kaplan-meier")

km.fit1
summary(km.fit1)
summary(km.fit1, times = c(30, 60, 90*(1:30)))

plot(km.fit1)
plot(km.fit1, conf.int = T, main = "KM")


km.fit2 <- survfit(Surv(LOS, censored) ~ Gender,
                   data = D2,
                   type = "kaplan-meier")

km.fit2
print(km.fit2)
summary(km.fit2)
summary(km.fit2, times = c(30, 60, 90*(1:30)))


plot(km.fit2)
plot(km.fit2, conf.int = T, main = "KM")

coxph <- coxph(Surv(LOS, censored) ~ Gender, data = D2 )
summary(coxph)