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These models are fitted by maximum likelihood and the likelihood value can be used as a relative measure of model fit. The problem is that, when modeling individual data, as in our study, the actual likelihood value depends on sample size and has no interpretation.
This coefficient can be interpreted as the goodness of the model in predicting the response from the covariates, similar to R 2 in linear regression. We have assumed approximate additivity in the proportion of variance explained by multiple factors, which is appropriate when these are not too large. In a second step of the two part model, the variability in pharmacy costs restricted to patients with prescriptions were estimated using variance components analysis of linear mixed models as before.