st: Analysis of scoring data???

[K Jensen <k_jensen@mailblocks.com>]

I have data involving assessment of the results of different medical 
imaging techniques, by different specialists.

For a hundred images, each has been assessed on a four point quality 
scale by six specialists. All the assessors have scored all the images 
and there are no missing values. The images fall into four types (there 
are different numbers of each type) and there are two types of 
clinician (three of each).

So the data looks like:-
Image_ID Type Clinician   C_type Score
      1   A  Clinican_1  Radiol   0
      1   A  Clinican_2  Radiol   3
      1   A  Clinican_3  Radiol   1
      1   A  Clinican_4  Radiog   2
      1   A  Clinican_5  Radiog   2
      1   A  Clinican_6  Radiog   1
...
    100   D  Clinican_6  Radiog   3

We are particularly interested in making inferences about the utility 
of the different types of image. One distinction is between the images 
scored at 1 or 2 (not useful in practice) v. 3 and 4 (useful).

So a summary could look like:-
image |         Score
 type |    0 or 1     2 or 3 |
-------+----------------------+
    A |        Na         Ya |
-------+----------------------+
    B |        Nb         Yb |
-------+----------------------+
    C |        Nc         Yc |
-------+----------------------+
    D |        Nd         Yd |
-------+----------------------+--------
Total  |        N          Y  | N+Y=600

Or we could consider using the average scores across clinicians for 
each image.

Types A and B use different variants of one imaging method, types C and 
D another.

We would like to test a priori hypotheses, such as "A and B are better 
than C and D" or "C is better than D" or "Clinicians of type one score 
more highly than those of type two".

I was tempted to do simple chi square tests based on the rows in the 
"tabulate" command, but have realised that that that would in a sense 
be overestimating the sample size by a factor of six, as we have 100 
different images assessed by six clinicians, not 600 different images.

I thought about logistic regression (xi:logit command) on the "score 0 
or 1" v. "1 or 2" outcome, but the results (either as beta coefficients 
or odds ratios) would be less easier to interpret than simple 
probabilities of falling into different categories.

I also thought about using the glm command and assuming binomial family 
data (for the dichotomous outcome).

As you will have guessed I am no statistician. How would a professional 
statistician like to see these data analyzed? I have come to realise as 
I write that this is a general question rather than a specifically 
Stata one, so I am sorry if this is an inappropriate query for this 
list.

Thankyou in advance and in hope,

Karin

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