I posted this a few days ago, but got no replies;
thought I'd try again, shorter
My data are time to readmission (failure) after
hospitalization, with censoring on death:
-> stset ftime, failure(radm)
failure event: radm != 0 & radm < .
obs. time interval: (0, ftime]
exit on or before: failure
------------------------------------------------------------------------------
424787 total obs.
0 exclusions
------------------------------------------------------------------------------
424787 obs. remaining, representing
95529 failures in single record/single failure data
1.07e+07 total analysis time at risk, at risk from t = 0
earliest observed entry t = 0
last observed exit t = 30
My model is
. streg `mycovars' , d(lnormal)
And my problem is:
. predict median, median time
. count if median<=30
30
So, though the data have 95,000+ failures in less than 30 days, my
model predicts only 30 such failures.
I get the same results with other parameterizations, though lognormal
seems the best fit. Either I don't understand what the model is doing,
or I'm not calculating the correct predicted value. Should I expect
this?
Thanks in advance for any insights.
Jeph
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