Cox Proportional Hazard with missing covariate data
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
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