ArticleInternational journal of health geographics2010
A power comparison of generalized additive models and the spatial scan statistic in a case-control setting.
Article in International journal of health geographics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- Environmental, social and behavioral risk factors in association with spatial clustering of childhood cancer incidence.Spatial and spatio-temporal epidemiology · 2023Article
- Estimating cumulative spatial risk over time with low-rank kriging multiple membership models.Statistics in medicine · 2022Article
- Knot selection for low-rank kriging models of spatial risk in case-control studies.Spatial and spatio-temporal epidemiology · 2022Article
- Exploring associations between prenatal exposure to multiple endocrine disruptors and birth weight with exposure continuum mapping.Environmental research · 2021Article
- The timing of geographic power.Statistics in medicine · 2020Article
- "Spatial heterogeneity of environmental risk in randomized prevention trials: consequences and modeling".BMC medical research methodology · 2019Article
- Spatial analyses of ALS incidence in Denmark over three decades.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2018Article
- Evaluation of the performance of smoothing functions in generalized additive models for spatial variation in disease.Cancer informatics · 2015Article
- Spatial-temporal analysis of non-Hodgkin lymphoma risk using multiple residential locations.Spatial and spatio-temporal epidemiology · 2012Article
- Adjusted significance cutoffs for hypothesis tests applied with generalized additive models with bivariate smoothers.Spatial and spatio-temporal epidemiology · 2011Article
- Power of permutation tests using generalized additive models with bivariate smoothers.Journal of biometrics & biostatistics · 2010Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundA common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics.
resultsThis research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases.
conclusionsThe GAM permutation testing methods provide a regression-based alternative to the spatial scan statistic. Across all hypotheses examined in this research, the GAM methods had competing or greater power estimates and sensitivities exceeding that of the spatial scan statistic.
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