[R] Cox Proportional Hazard with missing covariate data

[Philipp Rappold]

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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>