[R] predict.coxph and predict.survreg

[Mattia Prosperi]

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 David Winsemius <dwinsemius at comcast.net>:
>
> 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
>
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