Dear Prof. Therneau, Many thanks for this,
On 3/13/08, Terry Therneau <[EMAIL PROTECTED]> 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 > > > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
