I am looking to fit a Cox proportional hazards survival model. Looking at the K-M curve (below) for one variable (with 2 categories) it appears there is a change in hazard ratios at around day 110. I was thinking of modeling this with a change-point model.

I'm having trouble implementing it. I have defined days_ind as 1 if days>=110 and 0 otherwise. Then I run the model:

coxph(Surv(time=days,event=event2)~x*days_ind)

I get several warning messages about convergence and the results don't seem to make any sense.

Am I approaching this in the correct way? I thought of bringing in the interaction of x*days instead but this too does not converge and also leads to strange estimates.

I am looking to fit a Cox proportional hazard survival model. Looking at the K-M curve (below) for one variable (with 2 categories) it appears there is a change in hazard ratios at around day 110. I was thinking of modeling this with a change-point model.

I'm having trouble implementing it. I have defined days_ind as 1 if days>=110 and 0 otherwise. Then I run the model:

coxph(Surv(time=days,event=event2)~x*days_ind)

I get several warning messages about convergence and the results don't seem to make any sense.

Am I approaching this in the correct way? I thought of bringing in the interaction of x*days instead but this too does not converge and also leads to strange estimates.

I am looking to fit a Cox proportional hazards survival model. Looking at the K-M curve (below) for one variable (with 2 categories) it appears there is a change in hazard ratios at around day 110. I was thinking of modeling this with a changepoint model.

I'm having trouble implementing it. I have defined days_ind as 1 if days>=110 and 0 otherwise. Then I run the model:

coxph(Surv(time=days,event=event2)~x*days_ind)

I get several warning messages about convergence and the results don't seem to make any sense.

Am I approaching this in the correct way? I thought of bringing in the interaction of factor(x)*days instead but this too does not converge and also leads to strange estimates.

I am looking to fit a Cox proportional hazards survival model. Looking at the K-M curve (below) for one variable (with 2 categories) it appears there is a change in hazard ratios at around day 110. I was thinking of modeling this with a change-point model.

I'm having trouble implementing it. I have defined days_ind as 1 if days>=110 and 0 otherwise. Then I run the model:

coxph(Surv(time=days,event=event2)~x*days_ind)

I get several warning messages about convergence and the results don't seem to make any sense.

Am I approaching this in the correct way? I thought of bringing in the interaction of x*days instead but this too does not converge and also leads to strange estimates.