predict.coxph and predict.survreg

Dear all,

I'm struggling with predicting "expected time until death" for a  
coxph and
survreg model.

I have two datasets. Dataset 1 includes a certain number of people  
for which
I know a vector of covariates (age, gender, etc.) and their event  
times
(i.e., I know whether they have died and when if death occurred  
prior to the
end of the observation period). Dataset 2 includes another set of  
people for
which I only have the covariate vector. I would like to use Dataset  
1 to
calibrate either a coxph or survreg model and then use this model to
determine an "expected time until death" for the individuals in  
Dataset 2.
For example, I would like to know when a person in Dataset 2 will  
die, given
his/ her age and gender.

I checked predict.coxph and predict.survreg as well as the document "A
Package for Survival Analysis in S" written by Terry M. Therneau but  
I have
to admit that I'm a bit lost here.

On Nov 11, 2010, at 3:44 AM, Michael Haenlein wrote:

Dear all,

I'm struggling with predicting "expected time until death" for a coxph and
survreg model.

I have two datasets. Dataset 1 includes a certain number of people for
which
I know a vector of covariates (age, gender, etc.) and their event times
(i.e., I know whether they have died and when if death occurred prior to
the
end of the observation period). Dataset 2 includes another set of people
for
which I only have the covariate vector. I would like to use Dataset 1 to
calibrate either a coxph or survreg model and then use this model to
determine an "expected time until death" for the individuals in Dataset 2.
For example, I would like to know when a person in Dataset 2 will die,
given
his/ her age and gender.

I checked predict.coxph and predict.survreg as well as the document "A
Package for Survival Analysis in S" written by Terry M. Therneau but I
have
to admit that I'm a bit lost here.

The first step would be creating a Surv-object, followed by running a
regression that created a coxph-object, ?using dataset1 as input. So you
should be looking at:

?Surv
?coxph

There are worked examples in the help pages. You would then run predict() on
the coxph fit with "dataset2" as the newdata argument. The default output is
the linear predictor for the log-hazard relative to a mean survival estimate
but other sorts of estimates are possible. The survfit function provides
survival curve suitable for plotting.

(You may want to inquire at a local medical school to find statisticians who
have experience with this approach. This is ordinary biostatistics these
days.)

--
David.

Could anyone give me some advice on how this could be done?

Thanks very much in advance,

Michael



Michael Haenlein
Professor of Marketing
ESCP Europe
Paris, France

David Winsemius, MD
West Hartford, CT

Thanks for the comment, James!

The problem is that my initial sample (Dataset 1) is truncated. That  
means I
only observe "time to death" for those individuals who actually died  
before
end of my observation period. It is my understanding that this type of
truncation creates a bias when I use a "normal" regression analysis.  
Hence
my idea to use some form of survival model.

I had another look at predict.survreg and I think the option  
"response"
could work for me.
When I run the following code I get ptime = 290.3648.
I assume this means that an individual with ph.ecog=2 can be  
expected to
life another 290.3648 days before death occurs [days is the time  
scale of
the time variable).

Could someone confirm whether this makes sense?

lfit <- survreg(Surv(time, status) ~ ph.ecog, data=lung)
ptime <- predict(lfit, newdata=data.frame(ph.ecog=2), type='response')



On Thu, Nov 11, 2010 at 5:26 PM, James C. Whanger
<james.whanger at gmail.com>wrote:

Michael,

You are looking to compute an estimated time to death -- rather  
than the
odds of death conditional upon time.  Thus, you will want to use  
"time to
death" as your dependent variable rather than a dichotomous outcome (
0=alive, 1=death).   You can accomplish this with a straight forward
regression analysis.

Best,

Jim

On Thu, Nov 11, 2010 at 3:44 AM, Michael Haenlein <haenlein at escpeurope.eu 

wrote:

Dear all,

I'm struggling with predicting "expected time until death" for a  
coxph and
survreg model.

I have two datasets. Dataset 1 includes a certain number of people  
for
which
I know a vector of covariates (age, gender, etc.) and their event  
times
(i.e., I know whether they have died and when if death occurred  
prior to
the
end of the observation period). Dataset 2 includes another set of  
people
for
which I only have the covariate vector. I would like to use  
Dataset 1 to
calibrate either a coxph or survreg model and then use this model to
determine an "expected time until death" for the individuals in  
Dataset 2.
For example, I would like to know when a person in Dataset 2 will  
die,
given
his/ her age and gender.

I checked predict.coxph and predict.survreg as well as the  
document "A
Package for Survival Analysis in S" written by Terry M. Therneau  
but I
have
to admit that I'm a bit lost here.

Could anyone give me some advice on how this could be done?

Thanks very much in advance,

Michael



Michael Haenlein
Professor of Marketing