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I have developed a cox regression model to find out the parameters.

    fit_cox_Price_plan_device <-  coxph(Surv(df_life_4$Line.Tenure.In.Days,df_life_4$Churn.Flag==0)~df_life_4$Subscriber.Activity.Price.Plan.Code+df_life_4$Months.on.Price.Plan+df_life_4$Device+df_life_4$Months.on.Device + df_life_4$Total.MOU + df_life_4$Total.Active.Subscribers + df_life_4$GPS.Flag+df_life_4$WIFI.Flag + df_life_4$Multimedia.Flag + df_life_4$Price.Plan.Change.Flag)
    summary(fit_cox_Price_plan_device)
    pred_fit_cox_Price_plan_device<- survfit(fit_cox_Price_plan_device)
    summary(pred_fit_cox_Price_plan_device,times=c(1,30,60,90*(1:20)))

Now the result I obtain from this summary is like this:

time   n.risk  n.event  survival  lower 95%CI  upper 95% CI
1      165805   7316      0.963         0.962        0.964
30     149249   6109      0.929         0.928        0.930

And so on. My question is can anyone write an R code here that estimates the survival probability of each individual on the 500th day given that they already survived for more than 400 days?

Please help me out with that solution. Since I am stuck.

I have already tried this:

 predicted_days_dflife_4<-predict(pred_fit_cox_Price_plan_device,type='expected')
 basehaz_cox<-basehaz(fit_cox_Price_plan_device)
 predicted_days_dflife_4<-as.data.frame((exp(-53.12277))^exp(predicted_days_dflife_4)*2.4)
 df_life_4<- df_life_4[1:173258,]#Here 53.12277 is the approximation of the baseline hazard integrated upto time t but I know this is wrong
 individual_prob_on450<-cbind(predicted_days_dflife_4,df_life_4$Subscriber.Line.Id,df_life_4$Total.Active.Subscribers,df_life_4$Total.MOU,df_life_4$Months.on.Device,df_life_4$Months.on.Price.Plan,df_life_4$Subscriber.Activity.Price.Plan.Code,df_life_4$Market.Name)
 write.csv(individual_prob_on450,file = "individual_probability_on_450_day.csv")
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You can do this by dividing the survival probability at 500 days with the survival probability at 400 days or better by doing a landmark type of analysis in which you only consider the patients at risk at 400 days. Check these examples with the lung dataset from the survival package:

library("survival")

# Cox model in the whole dataset
fm <- coxph(Surv(time, status) ~ age + ph.karno, data = lung)

# Survival probabilities at 400 and 500 days
sfit <- summary(survfit(fm), times = c(400, 500))
sfit$surv[2] / sfit$surv[1]

# landamark approach; consider only the patients at risk at
# 400 days, fit the Cox model to them, and predict at 500 days
lung2 <- lung[lung$time >= 400, ] 
gm <- coxph(Surv(time, status) ~ age + ph.karno, data = lung2)
summary(survfit(gm), times = 500)
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  • $\begingroup$ But can the individual probability on a certain time = t not be obtained? $\endgroup$ – Nothing Sep 28 '18 at 8:25
  • $\begingroup$ I'm not sure what you mean... $\endgroup$ – Dimitris Rizopoulos Sep 28 '18 at 8:26
  • $\begingroup$ Person1 :probability of survival on 500th day given he has already lived for 400 days .The same for Person 2 and so on... $\endgroup$ – Nothing Sep 28 '18 at 8:27
  • $\begingroup$ More specifically for(each person):Calculate P(t=500|t>400) End loop. $\endgroup$ – Nothing Sep 28 '18 at 8:29
  • $\begingroup$ If you want to calculate these probabilities for specific persons, you can use the newdata argument of the survfit() function to provide their covariate information. $\endgroup$ – Dimitris Rizopoulos Sep 28 '18 at 8:30

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