Hi,
I have a Cox PH model that's large for my server, 120K rows, ~300 factors
with 3 levels each, so about 1000 columns. The 300 factors all pass a
preliminary test of association with the outcome. Solving this with cph from
Design takes about 3 hours. I have created the fit with x=T, y=T to save the
model data.
I want to validate the PH assumption by calling survplot(fit, gender=NA,
logt=TRUE, loglog=TRUE) for many of the factors (here gender is one column
name). Just creating this one plot takes 40m.
I'd be happy to sample from the fitted model to create these tests, or
figure out another way to check assumptions in the model.
Has anyone done something similar, or have other suggestions for tests that
scale better?
thanks.
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