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首页> 外文期刊>International Journal of Health Geographics >A power comparison of generalized additive models and the spatial scan statistic in a case-control setting
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A power comparison of generalized additive models and the spatial scan statistic in a case-control setting

机译：病例对照条件下广义加性模型和空间扫描统计量的功效比较

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Background 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.
机译：背景技术空间流行病学中一个常见的重要问题是在整个研究区域中测量和确定疾病风险的变化。在统计方法的应用中，问题分为两部分。首先，必须在整个研究区域内检测风险的空间变化，其次，必须正确识别风险增加或降低的区域。这些区域的位置可能为暴露的环境来源和疾病病因提供线索。适用于空间流行病学环境的一种统计方法是通用加性模型（GAM），可以将其与双变量LOESS平滑器一起应用，以将地理位置视为疾病状况的可能预测指标。应用此方法时的自然假设是对象的居住位置是否与结果相关联，即是否需要平滑项？置换检验是一种合理的假设检验方法，并在简单的替代假设下提供足够的功效。这些测试尚未与其他空间统计数据进行比较。结果本研究使用在三种替代假设下生成的模拟点数据来评估置换方法的属性，并将其与病例对照条件下流行的空间扫描统计数据进行比较。案例1是一个以圆形研究区域为中心的单个圆形簇。尽管GAM方法的估计并没有落后，但空间扫描统计数据的功效最高。情况2是位于圆形簇中心的单点源，情况3是在方形研究区域的水平轴中心的线源。每个点的线性度随着距该点的距离而线性减小。在案例2和案例3中，GAM方法优于扫描统计量。比较灵敏度（以正确识别为高风险或低风险的暴露源的比例来衡量），在三种情况下，GAM方法均优于扫描统计量。结论GAM排列测试方法为空间扫描统计量提供了基于回归的替代方法。在本研究中研究的所有假设中，GAM方法具有竞争性或更强的功效估计值和灵敏度，超过了空间扫描统计量。

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