Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Roger Newson <r.newson@imperial.ac.uk> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: RE: McNemar's test with clustering (Somers D comparisons) |

Date |
Mon, 26 Apr 2010 19:49:03 +0100 |

I hope this helps. Let me know if you have any further queries. Best wishes Roger Roger B Newson BSc MSc DPhil Lecturer in Medical Statistics Respiratory Epidemiology and Public Health Group National Heart and Lung Institute Imperial College London Royal Brompton Campus Room 33, Emmanuel Kaye Building 1B Manresa Road London SW3 6LR UNITED KINGDOM Tel: +44 (0)20 7352 8121 ext 3381 Fax: +44 (0)20 7351 8322 Email: r.newson@imperial.ac.uk Web page: http://www.imperial.ac.uk/nhli/r.newson/ Departmental Web page: http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/ Opinions expressed are those of the author, not of the institution. On 26/04/2010 19:12, Feiveson, Alan H. (JSC-SK311) wrote:

Roger - So in your proposed -somersd- analysis, would you use within, between or vonmises comparisons? With a continuous outcome, I would think that if you want to "remove" the twin effect, the comparisons should be within-cluster. However with a dichotomous predictor and outcome, there might be too many pairs with both responses the same, giving essentially no information. So in view of this would the vonmises comparisons with clustered standard errors be the best way? Or perhaps just between-cluster comparisons? Al Feiveson -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Roger Newson Sent: Monday, April 26, 2010 12:52 PM To: statalist@hsphsun2.harvard.edu Subject: Re: st: RE: McNemar's test with clustering Yes, you could treat twin pairs as a block. However, as we are sampling twin pairs from a population of twin pairs, I would still cluster by twin pair, whether I was using -clogit- or -somersd-. The -somersd- method has the advantage that it outputs a difference between proportions. I think more people understand those than understand odds ratios, although odds ratios are useful for estimating relative risks in a case-control study. Best wishes Roger Roger B Newson BSc MSc DPhil Lecturer in Medical Statistics Respiratory Epidemiology and Public Health Group National Heart and Lung Institute Imperial College London Royal Brompton Campus Room 33, Emmanuel Kaye Building 1B Manresa Road London SW3 6LR UNITED KINGDOM Tel: +44 (0)20 7352 8121 ext 3381 Fax: +44 (0)20 7351 8322 Email: r.newson@imperial.ac.uk Web page: http://www.imperial.ac.uk/nhli/r.newson/ Departmental Web page: http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/ Opinions expressed are those of the author, not of the institution. On 26/04/2010 18:36, Lachenbruch, Peter wrote:Could you treat the members of the twin pairs as a block in a randomized block fashion? The clogit idea sounds pretty good Tony Peter A. Lachenbruch Department of Public Health Oregon State University Corvallis, OR 97330 Phone: 541-737-3832 FAX: 541-737-4001 -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Laura Gibbons Sent: Monday, April 26, 2010 10:10 AM To: 'statalist@hsphsun2.harvard.edu' Subject: Re: st: RE: McNemar's test with clustering Sorry this wasn't clear. For this analysis, I'm just interested in the men as individuals, are their right and left sides different. If I had a continous outcome (and no twinship to consider), I'd use a paired t-test. But the sample happens to be (for other reasons) twins, so I need to adjust errors (p-values) for the correlation between twins. Pair Twin Left Right ----------------------------- 1 1 1 0 1 2 1 1 2 1 0 0 2 2 1 0 something like that, where I wan't to compare Left and Right, and Pair is a nuisance variable to me. thank you! Laura On Mon, 26 Apr 2010, Lachenbruch, Peter wrote:I seem to be missing something here. If you take the within-pair difference aren't you removing the pair effect? You can make the same argument for a dichotomous response. In this case the difference will be -1, 0, or 1. You could do a t-test on this (variance would be slightly off) or you could look at the table of responses and test if the proportion of -1s is the same as the proportion of +1s. May need to do this by hand, but should be simple. What is the clustering variable if not pairs? Tony Peter A. Lachenbruch Department of Public Health Oregon State University Corvallis, OR 97330 Phone: 541-737-3832 FAX: 541-737-4001 -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Laura Gibbons Sent: Sunday, April 25, 2010 6:39 PM To: statalist@hsphsun2.harvard.edu Subject: st: McNemar's test with clustering I'd like to do something like McNemar's test, -mcc-, where I'm comparing presence of two dichotomous traits in each person. [In this case, is a finding more common on the left side of the spine, compared to the right.] The problem is that the subjects are twins, in this analysis a nuisance parameter, but svyset or cluster(pair) are not options for mcc. For continuous outcomes I can get the equivalent of a paired t-test by computing the difference and then getting the p-values from the intercept in reg difference, cluster(pair) but I've not come up with anything along these lines either. Any guidance would be appreciated, thanks! -Laura ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Laura E. Gibbons, PhD General Internal Medicine, University of Washington Box 359780, Harborview Medical Center, 325 Ninth Ave, Seattle, WA 98104 phone: 206-744-1842, fax: 206-744-9917, Office address: 401 Broadway, Suite 5122 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Laura E. Gibbons, PhD General Internal Medicine, University of Washington Box 359780, Harborview Medical Center, 325 Ninth Ave, Seattle, WA 98104 phone: 206-744-1842, fax: 206-744-9917, Office address: 401 Broadway, Suite 5122 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: McNemar's test with clustering***From:*Laura Gibbons <gibbonsl@u.washington.edu>

**st: RE: McNemar's test with clustering***From:*"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>

**Re: st: RE: McNemar's test with clustering***From:*Laura Gibbons <gibbonsl@u.washington.edu>

**RE: st: RE: McNemar's test with clustering***From:*"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>

**Re: st: RE: McNemar's test with clustering***From:*Roger Newson <r.newson@imperial.ac.uk>

**RE: st: RE: McNemar's test with clustering (Somers D comparisons)***From:*"Feiveson, Alan H. (JSC-SK311)" <alan.h.feiveson@nasa.gov>

- Prev by Date:
**st: RE: Variable Creation** - Next by Date:
**st: Variable Creation** - Previous by thread:
**RE: st: RE: McNemar's test with clustering (Somers D comparisons)** - Next by thread:
**Re: st: McNemar's test with clustering** - Index(es):