Re: [R] survival analysis and censoring

From: Geoff Russell <geoffrey.russell_at_gmail.com>
Date: Thu, 13 Mar 2008 15:44:22 +1030

Dear Prof. Therneau,

Many thanks for this,

On 3/13/08, Terry Therneau <therneau_at_mayo.edu> wrote:
>
> In your particular case I don't think that censoring is an issue, at least not
> for the reason that you discuss. The basic censoring assumption in the Cox
> model is that subjects who are censored have the same future risk as those who
> were a. not censored and b. have the same covariates.
> The real problem with informative censoring are the covaraites that are not
> in the model; ones that I likely don't even know exist. Assume for instance
> that some unknown exposure X, Perth sunlight say, makes people much more likely
> to get both of the outcomes. Assume further that it matters, i.e., the study
> includes a reasonable number of people with and without this exposure. Then
> someone who has an early heart attack actually has a higher risk of colorectal
> cancer than a colleague of the same age/sex/followup who did not have a heart
> attack, the reason being that the HA guy is more likely to be from Perth.
>
> Your simulation went wrong by not actually accounting for time. You created
> an outcome table for CC & HD and added a random time vector to it. If someone
> would have had CC at 2 years and now has HD at 1 year, you can't just change the
> status to make them censored at 2. The gambling analogy would be kicking
> someone out of the casino just before they win -- it does odd things to the
> odds.

I'm still astonished that this is the explanation, but I've spent an hour playing with
my little R code model and this is exactly the problem. Score 1 for solid maths and 0 for my intuition.

Many Thanks,
Geoff

>
> Terry Therneau
>
>
>



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