Ontology highlight
ABSTRACT: Objective
To compare different strategies for analyzing longitudinal expenditure data that have a point mass at $0. We provide guidance on parameter interpretation, research questions, and model selection.Data sources, study design, and data collection
One-part models, uncorrelated two-part models, correlated conditional two-part (CTP) models, and correlated marginalized two-part (MTP) models have been proposed for longitudinal expenditures that often exhibit a large proportion of zeros and a distribution of continuous, highly right-skewed positive values. Guidance on implementing and interpreting each of these model is illustrated with an example of longitudinal (2000-2003) specialty care expenditures of veterans with hypertension, drawn from Veterans Administration data.Principal findings
The four strategies answer different research questions, are appropriate for different structures of data, and provide different results. If there is a point mass at $0, then the MTP model may be most useful if the primary interest is in mean expenditures of the entire population. A CTP model may be most useful if the primary interest is in the level of expenditures conditional on them being incurred.Conclusions
Researchers should consider which modeling strategy for longitudinal expenditure outcomes is both consistent with research aims and appropriate for the data at hand.
SUBMITTER: Smith VA
PROVIDER: S-EPMC6056585 | biostudies-literature | 2018 Aug
REPOSITORIES: biostudies-literature
Health services research 20180108
<h4>Objective</h4>To compare different strategies for analyzing longitudinal expenditure data that have a point mass at $0. We provide guidance on parameter interpretation, research questions, and model selection.<h4>Data sources, study design, and data collection</h4>One-part models, uncorrelated two-part models, correlated conditional two-part (CTP) models, and correlated marginalized two-part (MTP) models have been proposed for longitudinal expenditures that often exhibit a large proportion o ...[more]