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Mplus Discussion > Latent Variable Mixture Modeling >
 Philip Jones posted on Friday, December 15, 2006 - 11:12 am
Hello again! I'm looking at predictors of medication adherence at 1 year following a hospitalization. There are 4 binary manifest variables denoting adherence to 4 different meds. I am considering a latent class model with covariate effects on class membership. So I think an appropriate approach is something like:

categorical = medA medB medC medD;
classes = c(2);

type = mixture;

c#1 on x1 x2 x3 ...;

However, the caveat is that patients may have been prescribed any subset of the 4 meds, so not all meds are applicable for all patients. Setting the N/A med variables to missing doesn't seem right because the missingness is structural and is different than, say, missing because we didn't get follow-up on the patient.

In other software such as SAS I have handled similar situations by using "long" format data, with varying numbers of records (1 to 4) per patient, a single binary "adherence" outcome and a 4-level categorical variable indicating the med, and have used a two-level model clustering on patient. I'm uncertain if this is the best approach in Mplus and if so what the syntax would be. Appreciate your thoughts.
 Bengt O. Muthen posted on Friday, December 15, 2006 - 11:41 am
Challenging setting. I would think that the long data arrangement with varying number of lines per person is the same as wide with missing - at least this is the case in growth modeling. Apart from that, the key modeling issue though is that non-adherence in taking a med is not the same as missing. Hopefully, you have data on which meds each person was supposed to take. And, is the latent class variable an adherence/non-adherence distinction? It could also be a take more/less meds distinction, or both.
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