Niroshan <wnnperer <at> ucalgary.ca> writes: > 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 [snip] 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. >
I think that *in principle* (if you are using lme4, which is probably the most convenient option for dealing with crossed REs) you probably want ~ pathology + (pathology|reader)+(pathology|patient/eye) The fixed effect term says that pathologies may vary in their overall frequency. The first RE term says that different readers can vary, in a pathology-specific way (if they just differed overall in their sensitivity you would want (1|reader) instead); the second says that there is variance among eyes (within patients) in all pathologies (and that they may be correlated). A few cautions about this: * I'm not sure I got it right * You might want to forward this (along with my answer, so we're not starting from scratch) to r-sig-mixed-mod...@r-project.org , where there is more expertise in mixed models. * if you have the _same_ two readers for all of your patients (as opposed to two different readers chosen at random out of a large, possibly overlapping pool), then it isn't be practical to treat them as a random effect, no matter how much sense it makes philosophically -- use pathology*reader instead. * You may need a moderately large amount of data to fit this model ... ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
