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.