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Modeling zero-modified count and semicontinuous data in health services research part 2: case studies
Brian Neelon1, A James O'Malley2, Valerie A Smith3,4
1Department of Public Health Sciences, Medical University of South Carolina, Charleston, 29425, SC, U.S.A.. neelon@musc.edu.
Statistics in Medicine
|August 9, 2016
Summary
This tutorial explores methods for analyzing count and semicontinuous data with excess zeros. It details zero-inflated, hurdle, and marginalized two-part models using health services research examples.
Area of Science:
- Biostatistics
- Health Services Research
- Statistical Modeling
Background:
- Zero-modified count and semicontinuous data are common in health services research.
- Part 1 of this tutorial provided a general overview and background.
- This article focuses on practical applications and analytical approaches.
Purpose of the Study:
- To present three case studies demonstrating the analysis of zero-modified data.
- To illustrate various statistical methods for handling excess zeros in count and semicontinuous data.
- To provide practical examples relevant to health services research.
Main Methods:
- Case study 1: Analysis of zero-inflated longitudinal count data.
- Case study 2: Application of hurdle models for spatiotemporal count data.
- Case study 3: Use of marginalized two-part models for semicontinuous health expenditure data.
Main Results:
- Demonstrated effective application of zero-inflated models for longitudinal counts.
- Showcased the utility of hurdle models in spatiotemporal count data analysis.
- Illustrated the successful application of marginalized two-part models for semicontinuous data.
Conclusions:
- Various statistical models effectively address zero-modified count and semicontinuous data.
- The presented case studies offer practical guidance for health services researchers.
- Appropriate model selection is crucial for accurate analysis of complex health data.
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