John Sorkin wrote:
> Peter,
> Many thanks for your prompt reply.
>
> I think you may have been too quick to dismiss model2; there is no need for
> time to be negative. The time parameter is Surv represents survival, i.e.
> follow-up time. We usually start the follow-up clock at the time a subject is
> enrolled into a study, but this is not the only measure of survival time. One
> might argue that the clock should start at birth because the subject has
> survived to birth to plus the time represented by the ususal follow-up clock.
>
Yes, but your subjects logically cannot die before their recorded age if
I understand the setup correctly. I.e. you have left truncation --
people who die before enrolment are not recorded at all. This is at odds
with the proportional hazards assumption and it is a source of
(potential) grave error if the length of the "immortal" period is
related to the presence of the risk factor.
> John
>
> John Sorkin M.D., Ph.D.
> Chief, Biostatistics and Informatics
> Baltimore VA Medical Center GRECC,
> University of Maryland School of Medicine Claude D. Pepper OAIC,
> University of Maryland Clinical Nutrition Research Unit, and
> Baltimore VA Center Stroke of Excellence
>
> University of Maryland School of Medicine
> Division of Gerontology
> Baltimore VA Medical Center
> 10 North Greene Street
> GRECC (BT/18/GR)
> Baltimore, MD 21201-1524
>
> (Phone) 410-605-7119
> (Fax) 410-605-7913 (Please call phone number above prior to faxing)
> [EMAIL PROTECTED]
>
>
>>>> Peter Dalgaard <[EMAIL PROTECTED]> 2/5/2007 9:16 AM >>>
>>>>
> John Sorkin wrote:
>
>> I hope one and all will allow a stats question:
>>
>> When running a cox proportional hazards model ,there are two ways to
>> deal with age,
>> including age as a covariate, or to include age as part of the
>> follow-up time, viz,
>>
>> Age as a covariate:
>>
>> tetest1 <- list(time= c(4, 3,1,1,2,2,3),
>> status=c(1,NA,1,0,1,1,0),
>> age= c(0, 2,1,1,1,0,0),
>> riskfactor= c(0, 0,0,0,1,1,1))
>> fitagecovariate<-coxph( Surv(time, status) ~ age +riskfactor, test1)
>> fitagecovariate
>>
>> Age included as part of follow-up time:
>>
>> test2<-test1
>> test2$timeplusage<-test2$time+test2$age
>> fitagefollowup<-coxph( Surv(timeplusage, status) ~ riskfactor, test2)
>> fitagefollowup
>>
>> I would appreciate any thoughts about the differences in the
>> interpretation of the two models.
>> One obvious difference is that in the first model (fitagecovariate) one
>> can make inferences about age and in the second one cannot. I think a
>> second
>> difference may be that in the first model the riskfactor is assumed to
>> have values measured at the values of age where as in the second model
>> riskfactor is assumed to have given values throughout the subject's
>> life.
>>
>>
>>
> Model2 is plainly wrong, unless your times can be negative it represents
> long stretches of immortality (more obvious if all ages are about
> 80...)! Presumably, age is the age at entry, so a delayed-entry model
> could be appropriate (Surv(age,timeplusage,status)). If this
> modification is made, the main difference is that the time-since-entry
> scale can not (easily) have a separate effect in the delayed-entry
> model. If time is really is time since diagnosis or operation, then that
> could be badly wrong.
>
>
>> Your thoughts please.
>>
>> Thanks,
>> John
>>
>> R 2.1.1
>> windows XP
>>
>> John Sorkin M.D., Ph.D.
>> Chief, Biostatistics and Informatics
>> Baltimore VA Medical Center GRECC,
>> University of Maryland School of Medicine Claude D. Pepper OAIC,
>> University of Maryland Clinical Nutrition Research Unit, and
>> Baltimore VA Center Stroke of Excellence
>>
>> University of Maryland School of Medicine
>> Division of Gerontology
>> Baltimore VA Medical Center
>> 10 North Greene Street
>> GRECC (BT/18/GR)
>> Baltimore, MD 21201-1524
>>
>> (Phone) 410-605-7119
>> (Fax) 410-605-7913 (Please call phone number above prior to faxing)
>> [EMAIL PROTECTED]
>>
>> Confidentiality Statement:
>> This email message, including any attachments, is for the so...{{dropped}}
>>
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>>
>
>
>
--
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