Int J Health Geogr - A power comparison of generalized additive models and the spatial scan statistic in a case-control setting.


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CKGROUND: A 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.RESULTS: This 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.CONCLUSIONS: The 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.

Resumo Limpo

ckground common import problem spatial epidemiolog measur identifi variat diseas risk across studi region applic statist method problem two part first spatial variat risk must detect across studi region second area increas decreas risk must correct identifi locat area may give clue environment sourc exposur diseas etiolog one statist method applic spatial epidemiolog set general addit model gam can appli bivari loess smoother account geograph locat possibl predictor diseas status natur hypothesi appli method whether residenti locat subject associ outcom ie smooth term necessari permut test reason hypothesi test method provid adequ power simpl altern hypothesi test yet compar spatial statisticsresult research use simul point data generat three altern hypothes evalu properti permut method compar popular spatial scan statist casecontrol set case singl circular cluster center circular studi region spatial scan statist highest power though gam method estim fall far behind case singl point sourc locat center circular cluster case line sourc center horizont axi squar studi region linear decreas logodd distanc point gam method outperform scan statist case compar sensit measur proport exposur sourc correct identifi high low risk gam method outperform scan statist three casesconclus gam permut test method provid regressionbas altern spatial scan statist across hypothes examin research gam method compet greater power estim sensit exceed spatial scan statist

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