[R] survival analysis and censoring

[Geoff Russell]

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
>
>
>