I concur with your other reply - you have left-trunctated data.
Left-truncated: Any parts that failed before 2001 (start of study) are not
included in the study.
Left-censored: Parts that failed before the study start are included, but
with
an indeterminate failure date ("< 20001").
(1) Makes sense. Another approach is to use
the time since study entry and include the age
of the part in the model. A related discussion
here: http://tolstoy.newcastle.edu.au/R/e2/help/07/02/9831.html
(2) It is left-truncation. A part is observed only if it has
survived until study entry. Of co
Hi,
In fact, you have left-truncated observations.
What timescale do you use, time 0 is the
study entry, or when the wear-part has been used for the
first time?
If it is the latter, you can specify the "age" of the wear part
at study entry in Surv(). For example, if a wear part has been
used fo
Hi,
Arthur, thanks a lot for your super-fast reply!
In fact I am using the time when the part has been used for the first time, so
your example should work in my case.
Moreover, as I have time-variant covariates, the example should look like this
in my specific case:
start stopstatus te
Dear friends,
I have used R for some time now and have a tricky question about the
coxph-function: To sum it up, I am not sure whether I can use coxph in
conjunction with missing covariate data in a model with time-variant
covariates. The point is: I know how "old" every piece that I oberserve
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