R Under development (unstable) (2023-08-12 r84939 ucrt) -- "Unsuffered Consequences"
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> library(survival)
> 
> # Strata by covariate interactions, a case pointed out in early 2011
> #  by Frank Harrell, which as it turns out had never been computed
> #  correctly by any version of the package.  Which shows how often this
> #  case arises in practice.
> #
> aeq <- function(x, y, ...) all.equal(as.vector(x), as.vector(y))
> fit1 <- coxph(Surv(time, status) ~ wt.loss + age*strata(sex) + strata(ph.ecog),
+               data=lung)
> tdata <- data.frame(wt.loss=c(10,5,0,10, 15,20,25),
+                     age    =c(50,60,50,60,70,40,21),
+                     sex    =c(1,1,2,2,1,1,1),
+                     ph.ecog=c(0,0,1,1,2,2,2))
> surv1 <- survfit(fit1, newdata=tdata)
> 
> fit2 <- coxph(Surv(time, status) ~ wt.loss + age + I(age*0), data=lung, 
+               init=fit1$coef, iter=0, subset=(sex==1 & ph.ecog==0))
> fit2$var <- fit1$var
> 
> surv2 <- survfit(fit2, newdata=list(wt.loss=c(10,5), age=c(50,60)))
> s1 <- surv1[1:2]
> aeq(s1$surv, surv2$surv) #first a vector, second a matrix
[1] TRUE
> aeq(s1$std.err, surv2$std.err)
[1] TRUE
> aeq(s1[1]$time, surv2$time)
[1] TRUE
> aeq(s1[1]$n.event, surv2$n.event)
[1] TRUE
> 
> fit3 <- coxph(Surv(time, status) ~ wt.loss + age + I(age*1),
+               data=lung, init=fit1$coef, iter=0,
+               subset=(sex==2 & ph.ecog==1))
> fit3$var <- fit1$var
> surv3 <- survfit(fit3, newdata=list(wt.loss=c(0,10), age=c(50,60)))
> aeq(surv1[3:4]$surv, surv3$surv)
[1] TRUE
> aeq(surv1[3:4]$std, surv3$std)
[1] TRUE
> 
> fit4 <- coxph(Surv(time, status) ~ wt.loss + age + I(age*0),
+               data=lung, init=fit1$coef, iter=0,
+               subset=(sex==1 & ph.ecog==2))
> fit4$var <- fit1$var
> surv4 <- survfit(fit4, newdata=list(wt.loss=c(15,20,25), age=c(70,40,21)))
> 
> aeq(surv1[5:7]$surv, surv4$surv)
[1] TRUE
> aeq(surv1[5:7]$std.err, surv4$std.err)
[1] TRUE
> aeq(surv1[5]$n.risk, surv4$n.risk)
[1] TRUE
>              
> 
> proc.time()
   user  system elapsed 
   1.35    0.12    1.48