ArticleJournal of applied statistics2022
Zero-inflated models for adjusting varying exposures: a cautionary note on the pitfalls of using offset.
Article in Journal of applied statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- How Duffy Blood Group (FY) Polymorphism and Age Modulate Vivax Malaria Risk at the Community Level: A Population-based Retrospective Cohort Study in the Amazon.The Journal of infectious diseases · 2026Article
- Determining factors associated with the number of oocytes with appropriate morphology in infertile women: A cross-sectional study.International journal of reproductive biomedicine · 2025Article
- An extensive re-evaluation of evidence and analyses of the Randomised Badger Culling Trial (RBCT) I: Within proactive culling areas.Royal Society open science · 2024Article
- 1965 US Voting Rights Act Impact on Black and Black Versus White Infant Death Rates in Jim Crow States, 1959-1980 and 2017-2021.American journal of public health · 2024Article
- Evaluation of post-discharge engagement for emergency department patients with opioid use history who received telehealth recovery coaching services.Substance abuse treatment, prevention, and policy · 2023Article
- A comparison of zero-inflated and hurdle models for modeling zero-inflated count data.Journal of statistical distributions and applications · 2021Review
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Authors and funding
1 author.
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Abstract
Zero-inflated count data are frequently encountered in public health and epidemiology research. Two-parts model is often used to model the excessive zeros, which are a mixture of two components: a point mass at zero and a count distribution, such as a Poisson distribution. When the rate of events per unit exposure is of interest, offset is commonly used to account for the varying extent of exposure, which is essentially a predictor whose regression coefficient is fixed at one. Such an assumption of exposure effect is, however, quite restrictive for many practical problems. Further, for zero-inflated models, offset is often only included in the count component of the model. However, the probability of excessive zero component could also be affected by the amount of 'exposure'. We, therefore, proposed incorporating the varying exposure as a covariate rather than an offset term in both the probability of excessive zeros and conditional counts components of the zero-inflated model. A real example is used to illustrate the usage of the proposed methods, and simulation studies are conducted to assess the performance of the proposed methods for a broad variety of situations.
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