> From: thern...@mayo.edu
> To: james.whan...@gmail.com
> Date: Mon, 15 Nov 2010 08:43:04 -0600
> CC: r-help@r-project.org
> Subject: Re: [R] ... predict.coxph
>
> >If you are looking at radioactive decay maybe but how ofte
>If you are looking at radioactive decay maybe but how often do
>you actually see exponential KM curves in real life?
Exponential curves are rare. But proportional hazards does not imply
exponential.
> A trial design
could in fact try to get all the control sample to "event" at the same
time
> Date: Fri, 12 Nov 2010 16:08:57 -0600
> From: thern...@mayo.edu
> To: james.whan...@gmail.com
> CC: r-help@r-project.org; haenl...@escpeurope.eu
> Subject: Re: [R] predict.coxph
>
> Jim,
> I respectfully disagree, a
Jim,
I respectfully disagree, and there is 5 decades of literature to back
me up. Berkson and Gage (1950) is in response to medical papers that
summarized surgical outcomes using only the observed deaths, and shows
important failings of the method. Ignoring the censored cases usually
gives bias
Terry,
My point was that if you are asking the question: What is the average time
to death based on a set of variables? The only logical approach for
calculating actual time to death is to use uncensored cases, because we do
not know the time to death for the censored cases and can only estimate
Since I read the list in digest form (and was out ill yesterday) I'm
late to the discussion.
There are 3 steps for predicting survival, using a Cox model:
1. Fit the data
fit <- coxph(Surv(time, status) ~ age + ph.ecog, data=lung)
The biggest question to answer here is what covariates you wish
David, Mattia, James -- thanks so much for all your helpful comments!
I now have a much better understanding of how to calculate what I'm
interested in ... and what the risks are of doing so.
Thanks and all the best,
Michael
On Thu, Nov 11, 2010 at 7:33 PM, David Winsemius wrote:
>
> On Nov 11,
On Nov 11, 2010, at 12:14 PM, Michael Haenlein wrote:
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 understandin
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 "nor
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
reg
Thanks very much for your answers, David and Mattia.
I understand that the baseline hazard in a Cox model is unknown and that
this makes the calculation of expected survival difficult.
Does this change when I move to a survreg model instead?
I think I'm OK with estimating a Cox model (or a survre
Indeed, from the predict() function of the coxph you cannot get
directly "time" predictions, but only linear and exponential risk
scores. This is because, in order to get the time, a baseline hazard
has to be computed and it is not straightforward since it is implicit
in the Cox model.
2010/11/11
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 thei
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
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