Inference Based on Kernel Estimates of the Relative Risk Function in Geographical Epidemiology
Inference Based on Kernel Estimates of the Relative Risk Function in Geographical Epidemiology
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DOI：
10.1002/bimj.200810495
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发表时间：
2009-02-01
影响因子：
1.7
通讯作者：
Davies, Tilman M.
中科院分区：
文献类型：
--
作者：
Hazelton, Martin L.;Davies, Tilman M.
在地理流行病学中，核平滑是一种从病例和对照的位置数据估计相对风险面的常用方法。通过绘制公差等高线，可以方便地解释这些表面，这些等高线突出了风险足够高的区域，以拒绝单位相对风险的零假设。以前曾建议使用蒙特卡罗随机化试验来计算这些容忍间隔。我们研究了一种计算便宜的替代方案，其中容差区间是从渐近理论推导出来的。我们还使用基于核风险面的统计来检验异质性风险的全局测试的性能，特别注意测试功率上平滑参数的选择。
Kernel smoothing is a popular approach to estimating relative risk surfaces from data on the locations of cases and controls in geographical epidemiology. The interpretation of such surfaces is facilitated by plotting of tolerance contours which highlight areas where the risk is sufficiently high to reject the null hypothesis of unit relative risk. Previously it has been recommended that these tolerance intervals be calculated using Monte Carlo randomization tests. We examine a computationally cheap alternative whereby the tolerance intervals are derived from asymptotic theory. We also examine the performance of global tests of hetereogeneous risk employing statistics based on kernel risk surfaces, paying particular attention to the choice of smoothing parameters on test power.