Dear R Users, 

I have a question based on my research. I am analyzing reader-based diagnostic data set.  My study involves diabetic patients who were evaluated for treatable diabetic retinopathy based on the presence or absence of two pathologies in their eyes.  Pathologies were identified using the clinical examination (Gold standard method). In addition it can be identified by taking digital images of patients’ eyes and this method is cost effective. Finally two readers go over the images independently and patients are diagnosed as either positive or negative for the pathologies. 
My objective is, estimation the sensitivity and specificity of reader-based diagnostic method. 

I am going to fit multivariate probit model. But the problem has complex correlation structure. We have three different correlation: readers results are correlated, patients left and right eyes are correlated and pathologies are correlated since all based on the retina in the eye. 

Could anyone help me out how to address these correlations in a model using random effects? 

Also I think patients and readers are crossed each other since each reader go over each patients’ images. And eye eyes are nested with patients and pathologies are nested with in the eye.  Is this crossed and nested interpretation true?  If then how can I include these effects as random terms to the model? 

My response is readers ‘ diagnosed values. Per patient I have 8 values (2 pathologies, left and right eye and 2 readers) 
Explanatory variables are actual disease status of each pathology for left and right eyes. 

Thanks  

 
Niroshan Withanage 
Department of Mathematics and Statistics
Graduate Studies Office MS 344
Faculty of Science 
University of Calgary 
2500 University Drive N.W., Calgary
AB, T2N 1N4,CANADA
Tel:+1 (403) 210-8496
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