(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 course, if you reset the clock
at study entry, there's no delayed entries anymore.
Philipp Rappold wrote:
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 stop status temp humid
5 6 0 32 43
6 7 1 34 42
Just two more things:
(1) I am quite a newbie to cox-regression, so I wonder what you think about the approach
that I mentioned above? Don't worry, I won't nail you down to this, just want to make
sure I am not totally "off track"!
(2) I don't think that you'd call this "left-truncated" observations, because I
DO know the time when the part was used for the first time, I just don't have covariate
values for its whole time of life, e.g. just the last two years in the example above.
Left truncation in my eyes would mean that I did not even observe a specific part, e.g.
because it has died before the study started.
Again, thanks a lot, I'll be happy to provide valuable help on this list as
soon as my R-skills are advancing.
All the best
Philipp
Arthur Allignol wrote:
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 for 5 years before study entry, and "dies" 2 years after,
the data will look like that:
start stop status
5 7 1
Hope this helps,
Arthur Allignol
Philipp Rappold wrote:
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 is, but do not have fully historical information about the
corresponding covariates. Maybe you have some advice for me, although
this problem might only be 70% R and 30% statistically-related. Here's
a detailled explanation:
SITUATION & OBJECTIVE:
I want to analyze the effect of environmental effects (i.e.
temperature and humidity) on the lifetime of some wear-parts. The
study should be conducted on a yearly basis, meaning that I have
collected empirical data on every wearpart at the end of every year.
DATA:
I have collected the following data:
- Status of the wear-part: Equals "0" if part is still alive, equals
"1" if part has "died" (my event variable)
- Environmental data: Temperature and humidity have been measured at
each of the wear-parts on a yearly basis (because each wear-part is at
a different location, I have different data for each wear-part)
PROBLEM:
I started collecting data between 2001 and 2007. In 2001, a vast
amount of of wearparts has already been in use. I DO KNOW for every
part how long it has been used (even if it was employed before 2001),
but I DO NOT have any information about environmental conditions like
temperature or humidity before 2001 (I call this semi-left-censored).
Of course, one could argue that I should simply exclude these parts
from my analysis, but I don't want to loose valuable information, also
because the amount of "new parts" that have been employed between 2001
and 2007 is rather small.
Additionally, I cannot make any assumption about the underlying
lifetime distribution. Therefore I have to use a non-parametrical
model for estimation (most likely cox).
QUESTION:
From an econometric perspective, is it possible to use Cox
Proportional Hazard model in this setting? As mentioned before, I have
time-variant covariates for each wearpart, as well as what I call
"semi-left-censored" data that I want to use. If not, what kind of
analysis would you suggest?
Thanks a lot for your great help, I really appreciate it.
All the best
Philipp
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
