ArticleSpatial and spatio-temporal epidemiology2022
Knot selection for low-rank kriging models of spatial risk in case-control studies.
Article in Spatial and spatio-temporal epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Modeling Historic Arsenic Exposures and Spatial Risk for Bladder Cancer.Statistics in biosciences · 2024Article
- Modeling historic environmental pollutant exposures and non-Hodgkin lymphoma risk.Environmental research · 2023Article
- Estimating mixture effects and cumulative spatial risk over time simultaneously using a Bayesian index low-rank kriging multiple membership model.Statistics in medicine · 2022Article
- Estimating cumulative spatial risk over time with low-rank kriging multiple membership models.Statistics in medicine · 2022Article
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2 authors.
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Abstract
Many spatial analysis methods have been used to identify potential geographic clusters of disease in case-control studies. Low-rank kriging (LRK) models reduce the computational burden in generalized additive models by using a set of knot locations instead of the observed subject locations for estimating spatial risk. However, there is little guidance regarding selection of the number and location of the knots in case-control studies. We perform an extensive simulation study that compares a commonly-used method of knot selection in LRK models with two proposed methods and varies the number of knots. We find the commonly-used method is vastly outperformed by those that consider the locations of cases. We find that the Teitz and Bart heuristic allows the highest spatial sensitivity and power to detect zones of elevated risk, and recommend its use with a number of knots as close to the number of case locations as computation time will allow.
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