On Mar 18, 2012, at 3:54 PM, Thomas Lumley wrote:
On Mon, Mar 19, 2012 at 6:34 AM, David Winsemius <dwinsem...@comcast.net
> wrote:
On Mar 16, 2012, at 1:09 PM, niloo javan wrote:
hi
i want to analyze Right Censore-Length bias data under cox model
with
covariate.
what is the package ?
I initially left this question alone because I thought there might be
viewers for whom it all made perfect sense. After two days that
probability
seems to be declining. The problem I had was the meaning of "length
bias
data". Are you talking about a non-proportional effect in which the
assumption of a constant hazard ratio over time is false and other
methods
are needed. If that is correct, then you should get a copy of
Therneau and
Grambsch's "Modeling Survival Data" and study the chapter on
"Functional
Form'. The package would be "survival".
Length-biased sampling is what you get when you take a cross-sectional
sample of an ongoing process -- long intervals are over-represented.
Thank you Thomas;
For example people who have survived to age 75 might be
systematically different with respect to both the distribution of
cardiovascular risk factors and their impact on the event of interest
(AMI. CV death, or all-cause mortality) than persons at age 45. And
that would also not take into account the fact those risk factors
might have changed over the interval from age 45 to age 75 in the
survivors?
If the arrival time is known for everyone in the sample, the usual Cox
model facilities for left truncation apply. If the arrival times are
not known it would be much more difficult, and would probably need
parametric modelling.
Am I correct in thinking that additional assumptions about the
"length-bias" would need to be explicitly stated or modeled under a
set of plausible scenarios before progress in any framework could be
anticipated? It would seem that there could be many forms of such a
"length-bias".
--
David.
-thomas
--
Thomas Lumley
Professor of Biostatistics
University of Auckland
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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