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RE: st: clogit data format

From   "Nick Cox" <>
To   <>
Subject   RE: st: clogit data format
Date   Fri, 21 Mar 2008 17:27:41 -0000

I don't understand what normal and abnormal mean here, but otherwise 
this now sounds quite like a standard analysis of variance. 


Margaret R Grove

Thanks for the references, Nick.  I'm afraid I'm mostly a programmer and

do fairly basic analyses.  I can fairly well manipulate data with Stata 
into whatever shape is needed and have done lots of automated table 
generation.  I'm now into some stuff I'm not so comfortable with and 
which I've been asked to do with the choices having been pre-defined.  
That makes it difficult to respond to your questions.  However, it's 
most helpful to me that you're asking them to give me more to chew on.  
Yes, the original ratings were not dichotomized.  However, the number of

abnormal ratings is small in comparison to the "normals" so in the 4 
cases I'm looking at it probably does make sense.  We looked at the data

in many ways before distilling it down to kappas between reader pairs.  
The conditional logistic regression is, as I understand it, an attempt 
to obtain a p-value describing the distribution of responses between 
normal and abnormal to satisfy reviewers' requests.


Nick Cox wrote:
> Whoa! What is this "before we dichotomised it"? If you mean that your
> and 1s 
> are not the original ratings aren't you just throwing away
> Anyway, I have never understood all the enthusiasm for kappa, which
> despite its clear 
> definition is just a single summary measure. I have to suspect that
> lure of a single, supposedly simple summary, which comes wrapped up
> nicely with a P-value attached, sometimes triumphs over the challenge
> looking at the fine structure of agreement (or more precisely
> disagreement). 
> I don't know what your precise problem is but I have tackled what may
> similar ones. For example, in a couple of papers I have looked at
> problems in the Earth sciences in which several methods were used to
> measure what should be the same thing. Scientifically, putting a
> number on the strength of overall agreement has never seemed a
> useful thing to do. If overall agreement is extremely high, it is
> that the methods do all agree very well, but what's more typical in my
> experience is that the agreement is moderate or worse. In that
> the real challenge is to try to identify (e.g.) whether one or two
> methods are really out of line with the others. Naturally it need not
> a voting matter if one method is in some sense known to be very good
> (even a 'gold standard') and the others are poor. Admittedly if you
> dealing with the ratings given by various medics then implying that
> or more may not be so competent could be a difficult matter. 
> There is more in the same spirit (statistically, not politically) at 
> SJ-4-3  gr0005  . . . . .  Speaking Stata: Graphing agreement and
> disagreement
>         . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
> J. Cox
>         Q3/04   SJ 4(3):329--349                                 (no
> commands)
>         how to select the right graph to portray comparison or
>         assessment of agreement or disagreement between data
>         measured on identical scales
> which is now in the public domain via 
> and in 
> Cox, N.J. 2006. Assessing agreement of measurements and predictions in
> geomorphology. Geomorphology 76: 332-346                 
> doi:10.1016/j.geomorph.2005.12.001       
> which may or may not be accessible to you. 
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