R help - Mar 2008 - survival analysis and censoring

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.
       Terry Therneau

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
>
>
>