[R] comparing SAS and R survival analysis with time-dependent covariates

[AO_Statistics]

Thomas Lumley-2 wrote:
> 
> [...]
> 
> The warning and error messages are correct here.  Look at the point
> estimate. It's a log hazard ratio of about 20 in one case and about
> -20 in the other case.  The true partial maximum likelihood estimator
> is infinite. The estimated standard errors are meaningless, since the
> partial likelihood isn't close to quadratic at the maximum.
> 
> [...]
> 
I see. It explains the results for these testing data sets.

But, with my real data set I get these results :

With SAS :

estimate FERM : 1.47654
se : 0.03117
Pr > Khi 2 : <.0001
hazard ratio : 4.378
convergence status : "Convergence criterion (GCONV=1E-8) satisfied." 

This time, the hazard ratio is not big. The maximum of the partial
likelihood seems to be reached.
The program takes about 45 seconds to finish computation. My sample contains
6588 observations with a lot of ties (discrete time values).

With R :

I don't get any result. The program freezes and does not respond. I waited
for about 1 hour without a result.



So can I conclude in this case that the problem with the "coxph" function is
due to computation power rather than another algorithmic problem ?

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