[R] slow survfit -- is there a better replacement?

[gregory.bronner at barclayscapital.com]

Using R 2.10 on Windows:

I have a filtered database of 650k event observations in a data frame
with 20+ variables.

I'd like to be able to quickly generate estimate and plot survival
curves. However the survfit and cph() functions are extremely slow.


As an example: I tried 

results.cox<-coxph(Surv(duration, success) ~ start_time + factor1+
factor2+ variable3, data=filteredData)
#(took a few seconds)

plot(results.cox) 
#(never finished in an hour)

I also tried the cph() function, with similar results.


Is there some easier quick-and-dirty way of producing and plotting
survival curves for large data sets? I've seen some references on this
list that suggest that the underlying algorithm is O(numObs *
numSuccesses) and could be sped up. Has this been done?

Thanks,
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