Marginalized zero‐inflated Poisson models with missing covariates |
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| Authors: | Habtamu K. Benecha John S. Preisser Kimon Divaris Amy H. Herring Kalyan Das |
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| Affiliation: | 1. National Agricultural Statistics Service, USDA, Washington, USA;2. Department of Biostatistics, University of North Carolina, Chapel Hill, USA;3. Department of Pediatric Dentistry, University of North Carolina, Chapel Hill, USA;4. Department of Epidemiology, University of North Carolina, Chapel Hill, USA;5. Department of Statistical Science, Duke University, Durham, USA;6. Department of Statistics, University of Calcutta, Kolkata, India |
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| Abstract: | Unlike zero‐inflated Poisson regression, marginalized zero‐inflated Poisson (MZIP) models for counts with excess zeros provide estimates with direct interpretations for the overall effects of covariates on the marginal mean. In the presence of missing covariates, MZIP and many other count data models are ordinarily fitted using complete case analysis methods due to lack of appropriate statistical methods and software. This article presents an estimation method for MZIP models with missing covariates. The method, which is applicable to other missing data problems, is illustrated and compared with complete case analysis by using simulations and dental data on the caries preventive effects of a school‐based fluoride mouthrinse program. |
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| Keywords: | marginalized models missing at random missing data Monte Carlo EM zero‐inflation |
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