Dear Tim, Thank you very much for taking time giving me advices on my questions. I talked with my professor about this bootstrapping question whether to resample clinic or resample clinic + resample patients within clinic.
I was told that the second method might destroy the correlation structure between the patients within a clinic. So I am thinking if it is worthy that I do a simulation to compare the two kinds of bootstrapping method. I mean, is this comparision meaningful and is it worth of doing? What do you think? Thank you. Qian On 1 Apr 2005, Tim Hesterberg wrote: > Qian wrote: > >I talked with my advisor yesterday about how to do bootstrapping for my > >scenario: random clinic + random subject within clinic. She suggested that > >only clinic are independent units, so I can only resample clinic. But I > >think that since subjects are also independent within clinic, shall I > >resample subjects within clinic, which means I have two-stage resampling? > >Which one do you think makes sense? > > This is a tough issue; I don't have a complete answer. I'd > appreciate input from other r-help readers. > > If you randomly select clinics, then randomly select patients within > the clinics: > (1) by bootstrapping just clinics, you capture both sources of > variation -- the between-subject variation is incorporated in the > results for each clinic. > > (2) by bootstrapping clinics, then subjects within clinics, you > end up double-counting the between-subject variation > That argues for resampling just clinics. > > By analogy, if you have multiple subjects, and multiple measurements > per subject, you should just resample subjects. > > However, I'm not comfortable with this if you have a small number of > clinics, and relatively large numbers of patients in each clinic, and > think that the between-clinic variation should be small. Then it > seems better to resample both clinics and patients. > > I'm leery about resampling just clinics if there are a small number > of clinics. Bootstrapping isn't particularly effective for small > samples -- it is subject to skewness in small samples, and it > underestimates variances (it's advantages over classical methods > really show up with medium size samples). > There are remedies for the small variance, see > Hesterberg, Tim C. (2004), "Unbiasing the Bootstrap-Bootknife Sampling > vs. Smoothing", Proceedings of the Section on Statistics and the > Environment, American Statistical Association, 2924-2930 > www.insightful.com/Hesterberg/articles/JSM04-bootknife.pdf > > Tim Hesterberg > > ======================================================== > | Tim Hesterberg Research Scientist | > | [EMAIL PROTECTED] Insightful Corp. | > | (206)802-2319 1700 Westlake Ave. N, Suite 500 | > | (206)283-8691 (fax) Seattle, WA 98109-3044, U.S.A. | > | www.insightful.com/Hesterberg | > ======================================================== > Download the S+Resample library from www.insightful.com/downloads/libraries > > *************************************** Qian An Division of Biostatistics University of Minnesota (phone) 612-626-2263 (fax) 612-626-8892 Email: [EMAIL PROTECTED] ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
