I need to identify cases of type 2 diabetes in a health care database for a specific population using latent class analysis (LCA).
In addition to doctor's diagnosis of diabetes and the receipt of diabetes medications, I was planning to include glycosuria (sugar in urine; True/False) as one of the manifest (predictor) variables. But I found that:
30% of people in the cohort have data for urine test
of those having data for urine test, 5% have glycosuria
according to another study, %25 of those with confirmed diagnosis have data for urine test
the prevalence of glucosuria (any level) in the community is thought to be also 5%
Is it OK to use this variable in the model if it shows the expected distribution of values?
What important assumptions/conditions/checks that should be fulfilled by manifest variables in LCA?