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.
