# How do I interpret the result of individual survival probabilities in Survival Analysis in R?

I have developed a survival model using Cox Regression. The aim is to obtain individual probabilities of survival on a given date or time. Now the steps used are:

fit<-coxph(Surv(time,status=0)~$$X_1+X_2+X_3+X_4+.....+X_p$$)

summary(fit)
cox_fit<-survfit(fit)
cox_lp_predict<-predict(fit,type='lp')
basehaz_cox<-basehaz(fit)

individual_probability_at_t1<-as.data.frame(exp(-b_1)^(exp(cox_lp_predict)))
individual_probability_at_t2<-as.data.frame(exp(-b_2)^(exp(cox_lp_predict)))


fit<-coxph(Surv(time,status=0)~$$X_1+X_2+X_3+X_4+.....+X_p$$)Here status =0 because we want to analyse those observations where status=0, infact in my case status is churn flag=0$$=>$$ No churn,1$$=>$$Churn

cox_lp_predict<-predict(fit,type='lp')#This step is to find $$X_1\beta_1+X_2\beta_2+.....+x_p\beta_p$$ for each observation

basehaz_cox<-basehaz(fit)#Now let's say after obtaining the base hazard we get the cumulative base hazard i.e $$H_0(t)$$ at $$t=t_1$$ is $$=b_1$$

And let's say after obtaining the base hazard we get the cumulative base hazard using basehaz_cox<-basehaz(fit) i.e $$H_0(t)$$ at $$t=t_2$$ is $$=b_2$$

individual_probability_at_t1<-as.data.frame(exp(-b_1)^(exp(cox_lp_predict)))This is in line with the formula $$S(t|X)=exp(-\int_0^t h_0(t)dt)^{exp(\beta_1x_1+...\beta_px_p)}=S(t|X)=exp(-H_0(t))^{exp(\beta_1x_1+...\beta_px_p)}$$

Now my question is that when we obtain an appended dataframe i.e

Merged_individual_prob_at_t1_t2<-merge(individual_probability_at_t1$$Probability_at_t1,individual_probability_at_t2$$Probability_at_t2)


We get a table like this :

Serial No.   Prob(T>230)  Prob(T>360)   Prob(T>500)
1            0.1125       0.3455        0.3221
2            0.2344       0.3244        0.2877
3            0.2556       0.4456        0.3211


And so on.

My question exactly here for certain observations like the ones I show above the survival probability increases for a certain duration of time and then it reduces like the above table. Mind it most of the observations show a decreasing survival probability over the three time instances calculated but for some of them this increasing trend for a certain period comes and then it again starts reducing. My question is why does this happen? Is my approach correct? If not rectify me anywhere,please help.

• The question as it is presented now is pretty messy. You should run diagnostics and figure out exactly where the logic breaks down and ask a specific question for that specific step. – user2974951 Oct 2 '18 at 7:45
• First how to obtain individual probabilities of each individual at time t?Second, how do I interpret the increase and then decrease in survival probabilities of certain individuals over time? – Nothing Oct 2 '18 at 7:49

If you want to get individual probabilities from a fitted Cox model from the survival package in R, you can use the survfit() method, e.g.,
fm <- coxph(Surv(time, status == 0) ~ x1 + x2 + x3, data = your_data)