*Background*: at half-year follow up times for 4y, patients may switch to a different medication group. To account for this, I've converted survival data into counting process form. I want to compare survival curves for medication groups A, B, and C. I am using an extended Cox model but want to do pairwise comparisons of each hazard function or do stratified log-rank tests. `pairwise_survdiff`

throws an error because of the form of my data, I think.

*Example data*:

```
x<-data.frame(tstart=rep(seq(0,18,6),3),tstop=rep(seq(6,24,6),3), rx = rep(c("A","B","C"),4), death=c(rep(0,11),1))
x
```

*Problem*:

When using `survdiff`

in the `survival`

package,

`survdiff(Surv(tstart,tstop,death) ~ rx, data = x)`

I get the error:

`Error in survdiff(Surv(tstart, tstop, death) ~ rx, data = x) : Right censored data only`

I think this stems from the counting process form, since I can't find an example online that compares survival curves for time-varying covariates.

*Question*: is there a quick fix to this problem? Or, is there an alternative package/function with the same versatility to compare survival curves, namely using different methods? How can I implement stratified log-rank tests using `survidff`

on counting process form data?

**NOTE**: this was marked as a known issue in the survminer package, see github issue here, but updating survminer did not solve my issue, and using one time interval, tstop-tstart wouldn't be correct, since that would leave, e.g., multiple entries at 6 months rather than out to the actual interval of risk.