title: Modeling zero-modified count and semicontinuous data in health services research part 2: case studies.
abstract: This article is the second installment of a two-part tutorial on the analysis of zero-modified count and semicontinuous data. Part 1, which appears as a companion piece in this issue of Statistics in Medicine, provides a general background and overview of the topic, with particular emphasis on applications to health services research. Here, we present three case studies highlighting various approaches for the analysis of zero-modified data. The first case study describes methods for analyzing zero-inflated longitudinal count data. Case study 2 considers the use of hurdle models for the analysis of spatiotemporal count data. The third case study discusses an application of marginalized two-part models to the analysis of semicontinuous health expenditure data. Copyright © 2016 John Wiley & Sons, Ltd.
authors: Department of Public Health Sciences, Medical University of South Carolina, Charleston, 29425, SC, U.S.A.. neelon@musc.edu.
authors: Brian
authors: B
authors: Neelon
authors: Department of Biomedical Data Science and The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, 03766, NH, U.S.A.
authors: A James
authors: AJ
authors: O'Malley
authors: Center for Health Services Research in Primary Care, Durham VA Medical Center, Durham, 27705, NC, U.S.A.|Division of General Internal Medicine, Department of Medicine, Duke University, Durham, 27710, NC, U.S.A.
authors: Valerie A
authors: VA
authors: Smith
mesh_terms: D001699:Biometry; D005102:Health Expenditures; D006302:Health Services Research; D006801:Humans; D015233:Models, Statistical
keywords: Hurdle model; health services research; semicontinuous data; two-part model; zero inflation; zero-modified data