Skewed cost analyses

Costs in healthcare are notoriously skewed: a small percentage of patients could account for half the costs , (the 5-50 rule of thumb, see AHRQ), so modeling such variables needs extra ... care!
While transformations (logX, etc.) may 'squeeze' the original distribution of costs to 'look' better (more normally distributed), there is a lot that's left unattended when doing so (references below).
As an example, costs seen in the eConsult intervention are quite skewed:

A simple way to process actual costs is to add 1 (or 2) extra parameters to the classic mean and variance (or SD): the 'fatness of tail' (df for t-distribution) and the skew parameter; an intro into the 'how to' is here, presented at MMM-Storrs, CT).

Here I point to the Mplus way of testing such a model, an annotated syntax/output is posted at Researchgate, it shows how and why one models the costs differently.


References
1. Anderson, D., Villagra, V., Coman, E. N., Zlateva, I., Hutchinson, A., Villagra, J., & Olayiwola, J. N. (2018). A Cost-Effectiveness Analysis of Cardiology eConsults for Medicaid Patients. The American journal of managed care, 24(1), e9-e16. 
2. Loisel, P., et al., Cost‐benefit and cost‐effectiveness analysis of a disability prevention model for back pain management: a six year follow up study. Occupational and Environmental Medicine, 2002. 59(12): p. 807‐815.
3. Lee, S. and G. McLachlan, On mixtures of skew normal and skew t distributions. Advances in Data Analysis and Classification, 2013. 7(3): p. 241‐266.
4. Lee, S. and G. McLachlan, Finite mixtures of multivariate skew t distributions: some recent and new results. Statistics and Computing, 2014. 24(2): p. 181‐202.
5. Asparouhov, T. and B. Muthén, Structural Equation Models and Mixture Models With Continuous Nonnormal Skewed Distributions. Structural Equation Modeling: A Multidisciplinary Journal, 2015 : p. 1‐19.