A marginalized two-part model for semicontinuous data.


Journal Article

In health services research, it is common to encounter semicontinuous data characterized by a point mass at zero followed by a right-skewed continuous distribution with positive support. Examples include health expenditures, in which the zeros represent a subpopulation of patients who do not use health services, while the continuous distribution describes the level of expenditures among health services users. Semicontinuous data are typically analyzed using two-part mixture models that separately model the probability of health services use and the distribution of positive expenditures among users. However, because the second part conditions on a non-zero response, conventional two-part models do not provide a marginal interpretation of covariate effects on the overall population of health service users and non-users, even though this is often of greatest interest to investigators. Here, we propose a marginalized two-part model that yields more interpretable effect estimates in two-part models by parameterizing the model in terms of the marginal mean. This model maintains many of the important features of conventional two-part models, such as capturing zero-inflation and skewness, but allows investigators to examine covariate effects on the overall marginal mean, a target of primary interest in many applications. Using a simulation study, we examine properties of the maximum likelihood estimates from this model. We illustrate the approach by evaluating the effect of a behavioral weight loss intervention on health-care expenditures in the Veterans Affairs health-care system.

Full Text

Duke Authors

Cited Authors

  • Smith, VA; Preisser, JS; Neelon, B; Maciejewski, ML

Published Date

  • December 10, 2014

Published In

Volume / Issue

  • 33 / 28

Start / End Page

  • 4891 - 4903

PubMed ID

  • 25043491

Pubmed Central ID

  • 25043491

Electronic International Standard Serial Number (EISSN)

  • 1097-0258

Digital Object Identifier (DOI)

  • 10.1002/sim.6263


  • eng

Conference Location

  • England