Hi,
My original question:
I am analyzing survival data with time varying covariates. I am using the
coxph function with counting process format for the data. I have quit a lot
of data, fitting the model is not a problem but when I calculate an
expected survival for one individual with a certain covariate path this
takes a lot of memory and time. And I need to calculate the expected
survival for a lot of individuals each with a specific covariath path.
Would switching to parametric models help? I believe that these models are
not implemented in S-PLUS, does anyone have experiences with fitting and
analyzing parametric survival models (in S-PLUS) ?
***********************************************************
Parametric models for survival analyis are available in S-PLUS but not with
time varying covariates. It seems that you can do it with eiter Stata or
Limdep.
Thanks to those who responded,
cheers
Longhow.
Responses I had so far:
Parametric survival models are implimented in Splus (survReg or censorReg).
They don't handle time-dependent covariates: I am not aware of any
parametric
model that does. (It leads to a programming/bookkeeping morass, which no
one
seems to have had the energy to tackle).
Terry Therneau
**************************************
I'm pretty sure that survreg does standard parametric survival regression,
but I'm not sure whether it can cope with time-varying covariates. If not,
then the solution could lie in using GLMs to fit the survival analysis,
this is covered in a Chapter in McCullagh and Nelder. The idea is to model
the counting process data as a Poisson process with a log link and linear
predictor function of the form
offset(hazard(time))+coeff%*%covariates(time) .
Where hazard(time), is the baseline hazard function which you want to use
in your parametric model. This is justified by examining the two
likelihood functions and observing that they are proportional. In the case
of Cox proportional hazards, the offset(hazard(time)) is replaced by
factor(time), as no assumptions are made about the baseline hazard.
However, going back to your original problem, I've no reason to see why
this should be any more computationally efficient than coxph.
regards,
Simon Bond
********************************************
Parametric survival can be done using the censorReg function in
S-PLUS. There is a chapter or 5 on survivial analysis in the free
documentation that you can download from Insightful.
http://www.insightful.com/resources/doc/default.html
The survival stuff is in "Guide to statistics volume 2", the survival
stuff starts in chapter 8 and the parametric survival is in chapter 11.
hope this helps,
Greg Snow, PhD
*****************************************************
I believe they are, perhaps depending on what version of Splus you have.
Look for a function named censorReg(). It's in unix versions 5 and 6 for
sure.
-Don
Hi Longhow,
I use discrete-time models, eg binomial regressions with a cloglog link.
See: Prentice RL, Gloeckler LA, 1978. Regression analysis of grouped
survival data with application to breast cancer data. Biometrics, 34:
57-67.
It's straighforward to use and you won't have problems with predictions
(I guess !). The biggest problem is to turn the data into an appropriate
format. Moreover, you can't use a continuous time-dependent covariate,
or you will have to discretize it.
Hope this helps,
Renaud
|