On 03/24/2012 06:00 AM, r-help-requ...@r-project.org wrote:
I have been using the function 'svycoxph' in the Dr. Lumley's survey package
(version 3.26) to compute coefficient estimates for Cox regression.
I have noticed the p-values output are based on normal distribution (like in
coxph); however in svyglm (and in other software, such as Stata or SAS) the
p-values are computed via the t distribution with degrees of freedom equal to
the number of PSUs minus number of strata.
I am wondering why there is a difference here?
I'm not aware of any theory papers that back up the use of a
t-distribution. This is a Cox model, and "do what my Gaussian package
does" is not usually the best approach. I'm far from an expert in
survey work though, so I'll yeild to Thomas L for a definitive answer.
In the case of mixed effects models I see the exact same leaning
towards (approximate) REML vs ML; this is an area that I do know deeply
and and the "REML better than ML" arguments from linear mixed effects
models to NOT transfer over.
Terry Therneau
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