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 <dwinsem...@comcast.net>wrote:
>
> 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 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).
>>
>
> It is a prediction under specific assumptions underpinning a parametric
> estimate.
>
>
> Could someone confirm whether this makes sense?
>>
>
> You ought to confirm that it "makes sense" by comparing to your data:
> reauire(Hmisc); require(survival)
> <your code>
>
> > describe(lung[lung$status==1&lung$ph.ecog==2,"time"])
> lung[lung$status == 1 & lung$ph.ecog == 2, "time"]
> n missing unique Mean
> 6 0 6 293.7
>
> 92 105 211 292 511 551
> Frequency 1 1 1 1 1 1
> % 17 17 17 17 17 17
>
> > ?lung
>
> So status==1 is a censored case and the observed times are status==2
> > describe(lung[lung$status==2&lung$ph.ecog==2,"time"])
> lung[lung$status == 2 & lung$ph.ecog == 2, "time"]
> n missing unique Mean .05 .10 .25 .50 .75
> .90 .95
> 44 1 44 226.0 14.95 36.90 94.50 178.50 295.75
> 500.00 635.85
>
> lowest : 11 12 13 26 30, highest: 524 533 654 707 814
>
> And the mean time to death (in a group that had only 6 censored individual
> at times from 92 to 551) was 226 and median time to death among 44
> individuals is 178 with a right skewed distribution. You need to decide
> whether you want to make that particular prediction when you know that you
> forced a specific distributional form on the regression machinery by
> accepting the default.
>
>
>
>
>> 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.whan...@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 <
>>> haenl...@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
>>>>
>>>
>
> David Winsemius, MD
> West Hartford, CT
>
>
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