Modeling zero-modified count and semicontinuous data in health services research part 2: case studies
Corresponding Author
Brian Neelon
Department of Public Health Sciences, Medical University of South Carolina, Charleston, 29425 SC, U.S.A.
Correspondence to: Brian Neelon, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, U.S.A.
E-mail: [email protected]
Search for more papers by this authorA. James O'Malley
Department of Biomedical Data Science and The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, 03766 NH, U.S.A.
Search for more papers by this authorValerie A. Smith
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.
Search for more papers by this authorCorresponding Author
Brian Neelon
Department of Public Health Sciences, Medical University of South Carolina, Charleston, 29425 SC, U.S.A.
Correspondence to: Brian Neelon, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, U.S.A.
E-mail: [email protected]
Search for more papers by this authorA. James O'Malley
Department of Biomedical Data Science and The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, 03766 NH, U.S.A.
Search for more papers by this authorValerie A. Smith
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.
Search for more papers by this authorAbstract
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.
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