
Dear R-help,

I was comparing SAS (I do not know what version it is) and R (version 
2.6.0 (2007-10-03) on Linux) survival analyses with time-dependent 
covariates. The results differed significantly so I tried to understand 
on a short example where I went wrong. The following example shows that 
even when argument 'method' in R function coxph and argument 'ties' in 
SAS procedure phreg are the same, the results of Cox regr.  are 
different. This seems to happen when there are ties in the 
events/covariates times.

My question is what software, R or SAS, is more reliable for the 
survival analysis with time-dependent covariates or if you could point 
out a problem in the following example.

Example.   SAS gives HR=3.236:

data trythis;
input id days timedeli stat;
datalines;
  1    3        .5      1
  2   1.5        1      1
  3    6       1000     0
  4    8       1000     1
  5    8         1      0
  6    21      1000     1
  7    11        3      1
run;
proc phreg data=trythis;
  model days*stat(0)=deli/risklimits ties=exact;
  if  timedeli>days then deli=0; else deli=1;
run;

Example (continued).  R gives HR=3.91:

tmp = data.frame(id=c(1, 1, 2, 2, 3, 4, 5, 5, 6, 7, 7), start=c(0.0, 
0.5, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 3.0), end=c(0.5,  3.0,  
1.0,  1.5,  6.0,  8.0,  1.0,  8.0, 21.0,  3.0, 11.0), delir=c(0, 1, 0, 
1, 0, 0, 0, 1, 0, 0, 1), outcome=c(0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1))
tmp
surv = Surv(time=tmp$start, time2=tmp$end, tmp$outcome)
cphres = coxph(surv ~ tmp$delir, method="exact")
summary(cphres)[["coef"]]

After breaking a tie b/w an event and a time-dependent observation, R 
gives the same result as SAS.

tmp$end[2]=tmp$end[2] + .1
tmp
surv = Surv(time=tmp$start, time2=tmp$end, tmp$outcome)
cphres = coxph(surv ~ tmp$delir, method="exact")
summary(cphres)[["coef"]]

Thank you so much for time,

Svetlana






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