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Modeling zero‐modified count and semicontinuous data in health services research part 2: case studies
Author(s) -
Neelon Brian,
O'Malley A. James,
Smith Valerie A.
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7063
Subject(s) - count data , zero (linguistics) , computer science , data science , data analysis , longitudinal data , statistics , econometrics , data mining , mathematics , philosophy , linguistics , poisson distribution
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.