I thought about this some more, and I'm not sure that possibility is
to blame. In my time-dependent model, I don't think I'm doing
anything different than is done for transplant in the Stanford
Heart Study (the often used example for this kind of time-dependent
covariate). As in my case,
Kevin E. Thorpe wrote:
Peter Dalgaard wrote:
Kevin E. Thorpe wrote:
Dear List:
I have a data frame prepared in the couting process style for including
a binary time-dependent covariate. The first few rows look like this.
PtNo StartEnd Status Imp
1 1 0 608.0 0 0
From my experience, what you are seeing is almost certainly a patient
selection effect. (The number 1 reason for puzzling results is incorrect
coding
of a time-dependent covariate, but you appear to have been quite careful).
Assigning the implant as a non-time dependent covariate
On Tue, 2 Oct 2007, Kevin E. Thorpe wrote:
Kevin E. Thorpe wrote:
Peter Dalgaard wrote:
Kevin E. Thorpe wrote:
Dear List:
I have a data frame prepared in the couting process style for including
a binary time-dependent covariate. The first few rows look like this.
PtNo StartEnd
Dear List:
I have a data frame prepared in the couting process style for including
a binary time-dependent covariate. The first few rows look like this.
PtNo StartEnd Status Imp
1 1 0 608.0 0 0
2 2 0 513.0 0 0
3 2 513 887.0 0 1
4 3
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