seminars:stat:nov62025 [Department of Mathematics and Statistics, Binghamton University]

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<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematics and Statistics</WRAP>

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^  **DATE:**|Thursday, November 6, 2025 |
^  **TIME:**|1:30pm -- 2:30pm |
^  **LOCATION:**|WH 100E (Zoom talk)|
^  **SPEAKER:**|Harrison Quick, University of Minnesota|
^  **TITLE:**|Reliable rates in disease mapping|
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<WRAP centeralign>**Abstract**</WRAP>
When I worked at the CDC, my role was to produce county-level estimates of death rates due to heart disease and stroke stratified by demographic factors like age, race/ethnicity, and sex across the United States.  At one of my first meetings, my team lead asked a seemingly simple question: Which of these estimates were “reliable” and fit to be published in our reports and which – due to limited data – should be deemed “unreliable” and be suppressed in our reports?  Unbeknownst to me, that question would become a driving factor in my research for the next 10+ years.  On one hand, many health organizations have criteria for reliability based on the size of the numerators (deaths) and/or denominators (population size) – while we could have simply leaned on their guidance, doing so would lead to wide swaths of the US having “unreliable” estimates.  On the other hand, my training as a Bayesian spatial statistician suggested that we could use prior information (e.g., similarities between neighboring counties) to improve the precision and predictive performance of our estimates – while this would increase the reliability of the estimates, it might also run the risk of making the estimates too reliable.  This talk aims to describe my strategy for striking a balance between these two mindsets.  In particular, I will begin by introducing my criteria for declaring estimates of event rates as “reliable” based on the posterior distribution of the estimates.  I will then demonstrate how these criteria can be incorporated into the specification of spatial Bayesian models to avoid oversmoothing when analyzing small counts for the purpose of producing age-standardized estimates of county-level death rates due to acute myocardial infarction in Pennsylvania and tract-level heart disease death rates in Philadelphia.





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