Dear Terry,
Thank you for the extended explanation -- it's helpful.
Best,
John
________________________________________
From: Therneau, Terry M., Ph.D. [thern...@mayo.edu]
Sent: August 31, 2015 9:56 AM
To: r-help@r-project.org; Fox, John; Göran Broström
Subject: Re: using survreg() in survival package with "long" data
On 08/30/2015 05:00 AM, r-help-requ...@r-project.org wrote:
> I'm unable to fit a parametric survival regression using survreg() in the
> survival package with data in "counting-process" ("long") form.
>
> To illustrate using a scaled-down problem with 10 subjects (with data placed
> on the web):
>
As usual I'm a day late since I read digests, and Goran has already clarified
things. A
discussion of this is badly needed in my as yet unwrritten book on using the
survival
package. From a higher level view:
If an observation is interval censored (a,b) then one knows that the event
happened
between time "a" and time "b", but not when. The survreg routine can handle
interval
censored data since it is parametric (you need to integrate over the interval).
The
interval (-infinity, b) is called 'left censored' and the interval (a,
infinity) is 'right
censored'. Left censored data is rare in medical work, an example might be a
chronic
disease like rhuematoid arthritis where we know that the true disease onset was
some time
before the date it was first detected, and one is trying to deduce the duration
of disease.
Left truncation at time 'a' means that any events before time "a" are not in
the data
set. In a referral center like mine this includes any subjects who die before
they come
to us. The coxph model handles left truncation naturally via its counting
process
formulation. That same formulation also allows it to deal with time dependent
covariates. Accelerated failure time models like survreg can handle left
truncation in
principle, but they require that the values of any covariates are known from
time 0 --
even for a truncated subject. I have never added left-truncation to the
survreg code,
mostly because I have never needed it myself, but also because users would
immediately
think that they could accomplish time-dependent covariates by simply using a
long format
data set. Rather, each subject needs to be linked to a full covariate history,
which is a
bit more work.
So: coxph does left truncation but not left (or interval) censoring
survreg does interval censoring but not left truncation (or time
dependent covariates).
Terry T
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