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From |
sjsamuels@gmail.com |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: correlated data |

Date |
Tue, 1 Sep 2009 13:35:31 -0500 |

Why didn't people respond? Perhaps, because your question was confused, and you gave too little information. "how close (do they differ significantly?" These two questions are not the same, so your purpose is not clear. Is it to find if one or more of the alternatives is close enough to the standard to replace it? If so, the problem is one of equivalence-testing, supplemented with confidence intervals. If you really just want to ask if the new methods differ from the standard, you could do a pairwise multivariate hypothesis test that the three algorithms differ from the standard; this would take into account the correlation, but I don't see that this is useful on two counts: 1) a difference of one method would be masked by similarity of the other two; 2) ultimately you probably want a separate assessment for each of the three methods. If you want to know "how differnt", I would do the three pair-wise equivalence tests. However, confidence intervals might be equally informative. I would also not correct for multiple-comparisons, but again that depends on the purpose of your analysis. More: you are not clear whether each algorithm uses the same measurement information or whether the algorithms independently re-measure the scans. If the algorithms use the same measurement information, but differ mathematically, then a priori, they will give different results, no statistical inference necessary. If the algorithms do independently re-measure the scans, then you might find point comparisons of the original measurements useful. If the measurements are not automatic, but there is room for operator or other variation (such as in placing the scanned images, calibration), then the experiment should probably have included replication of the the methods on random, blinded, presentations of the same scans. I suspect that the pairwise plots (method vs method) will give you the best information. You could have perfect monotone, but non-linear, association, for example. Ultimately, the number of scans will determine the precision of any estimates of difference. -Steve On Tue, Sep 1, 2009 at 7:28 AM, Nick Cox<n.j.cox@durham.ac.uk> wrote: > You asked the same question on 23 August: > > <http://www.hsph.harvard.edu/cgi-bin/lwgate/STATALIST/archives/statalist > .0908/Author/article-1091.html> > > and received two quick but brief replies. > > Before repeating a question like this, it is usually best to think: Why > did I not get a detailed reply? Was my question not clear enough? Does > the list lack experts in this field? Should I rephrase the question? See > also > > <http://www.stata.com/support/faqs/res/statalist.html#noanswer> > > It is also best to explain why any replies you got do not help. > > I don't know the answer, but I do note that list members are often more > reluctant to give strategic advice than to answer specific Stata > questions. > > Nick > n.j.cox@durham.ac.uk > > Nikolaos Pandis > > We have a set of 3-D images constructed from cat scans, and we are > measuring volumes defined by certain anatomical points on the 3-D > images. > > The reconstruction/measuring technique is performed using 3 new types of > software and their results will be compared with the results of > validated/reference technique. > > The same reconstructions/cat scans are used for all techniques. > > The objective is to see how close (do they differ significantly?) the > volume values recorded by each technique are to the values recorded by > the reference technique. > > I was thinking along the lines of regression with the volume(continuous) > variable as the dependent variable and technique as the categorical > dependent variable with 4 levels. The reference level would be the the > standard/validated method. > > However, how would I account for the fact that the data is correlated > since all measurements for the 4 methods are taken from the same > reconstructions/scans? > > * > * 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/ > -- Steven Samuels sjsamuels@gmail.com 18 Cantine's Island Saugerties NY 12477 USA 845-246-0774 * * 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: RE: correlated data***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

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