Re: [R] svycoxph and test statistics
Thank you, Dr. Therneau... I got a similar answer from Dr. Lumley: On Mar 24, 2012, at 4:05 PM, Thomas Lumley wrote: As far as I know there isn't any theoretical justification for the t-distribution but it empirically works better. You can get tests with a t or F reference distribution easily with regTermTest. You can also get likelihood ratio tests that way, which appear to have slightly better small-sample performance than the standard Wald tests. - thomas On Mar 25, 2012, at 5:17 PM, Terry Therneau wrote: 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 __ 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.
Re: [R] svycoxph and test statistics
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 __ 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.
[R] svycoxph and test statistics
Hello, 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? Thank you very much, Chirag Patel Stanford University c...@stanford.edu __ 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.
