Does having expected distribution of values allow using a manifest variable with missing data in latent class analysis? 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:


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*30% of people in the cohort have data for urine test

*of those having data for urine test, 5% have glycosuria
Assume that:


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*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?
 A: Are diagnosis/receipt of medication (binary?/and glycosuria the only variables you have? Why do you need to do a LCA? I mean, if there is a diagnosis you should be reasonably certain that the patient has diabetes. That leaves you with four combinations of medication and glycosuria when there is no diagnosis of diabetes. Diabetes or even internal medicine is not my field, but it seems you might be better of just deciding how you should judge each of these combinations, especially the presence of glycosuria when there is no diagnosis or medications:
diabetes dx (regardless of other variables) = diabetes
diabetes medication (regardless of other variables) = diabetes
no dx and no med, but glycosuria = ? (probably undetected diabetes?)
none of the above = no diabetes
But in any case, I think that missing data should't be a problem in LCA. In the documentation to R package poLCA it says that missing data on manifest variables can be handled, so you should be good to go, I think. I think you might risk selection bias if there is an association between the test being taken and higher levels (more often glycosuria), but because glycosuria is most often caused by diabetes anyway (if I recall correctly) this shouldn't be a problem.
Give it a try, using 2 latent classes, and examine the results. Do they seem reasonable?
My guess is that the identified latent classes will be any positive of diagnosis/medication/glucosuria vs. none of them.
