Journal article
Reliable Event Rates for Disease Mapping
Journal of official statistics, v 40(2), pp 333-347
01 Jun 2024
PMID: 39364189
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
When analyzing spatially referenced event data, the criteria for declaring rates as "reliable" is still a matter of dispute. What these varying criteria have in common, however, is that they are rarely satisfied for crude estimates in small area analysis settings, prompting the use of spatial models to improve reliability. While reasonable, recent work has quantified the extent to which popular models from the spatial statistics literature can overwhelm the information contained in the data, leading to oversmoothing. Here, we begin by providing a definition for a "reliable" estimate for event rates that can be used for crude and model-based estimates and allows for discrete and continuous statements of reliability. We then construct a spatial Bayesian framework that allows users to infuse prior information into their models to improve reliability while also guarding against oversmoothing. We apply our approach to county-level birth data from Pennsylvania, highlighting the effect of oversmoothing in spatial models and how our approach can allow users to better focus their attention to areas where sufficient data exists to drive inferential decisions. We then conclude with a brief discussion of how this definition of reliability can be used in the design of small area studies.
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Details
- Title
- Reliable Event Rates for Disease Mapping
- Creators
- Harrison Quick (Corresponding Author) - University of MinnesotaGuangzi Song - Drexel University
- Publication Details
- Journal of official statistics, v 40(2), pp 333-347
- Publisher
- Sage
- Number of pages
- 15
- Grant note
- R01HL158802 / National Heart, Lung, And Blood Institute of the National Institutes of Health, United States County Health Rankings & Roadmaps program
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:001274846700001
- Scopus ID
- 2-s2.0-105004920080
- Other Identifier
- 991022202523204721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Social Sciences, Mathematical Methods
- Statistics & Probability