PDF(Pubmed)
Abstract：
Spatial cluster analyses are commonly used in epidemiologic studies of case-control data to detect whether certain areas in a study region have an excess of disease risk. Case-control studies are susceptible to potential biases including selection bias, which can result from non-participation of eligible subjects in the study. However, there has been no systematic evaluation of the effects of non-participation on the findings of spatial cluster analyses. In this paper, we perform a simulation study assessing the effect of non-participation on spatial cluster analysis using the local spatial scan statistic under a variety of scenarios that vary the location and rates of study non-participation and the presence and intensity of a zone of elevated risk for disease for simulated case-control studies. We find that geographic areas of lower participation among controls than cases can greatly inflate false-positive rates for identification of artificial spatial clusters. Additionally, we find that even modest non-participation outside of a true zone of elevated risk can decrease spatial power to identify the true zone. We propose a spatial algorithm to correct for potentially spatially structured non-participation that compares the spatial distributions of the observed sample and underlying population. We demonstrate its ability to markedly decrease false positive rates in the absence of elevated risk and resist decreasing spatial sensitivity to detect true zones of elevated risk. We apply our method to a case-control study of non-Hodgkin lymphoma. Our findings suggest that greater attention should be paid to the potential effects of non-participation in spatial cluster studies.
摘要：
空间聚类分析通常用于病例对照数据的流行病学研究中，以检测研究区域中的某些区域是否具有过多的疾病风险。病例对照研究容易受到潜在偏见的影响，包括选择偏见，这可能是由于符合条件的受试者未参与研究。然而,没有系统评估不参与对空间聚类分析结果的影响。在本文中,我们进行了一项模拟研究,评估了不参与对空间聚类分析的影响,在各种情景下,这些情景改变了不参与研究的位置和比率,以及模拟病例对照研究中疾病风险升高区的存在和强度.我们发现，与病例相比，对照者参与程度较低的地理区域会大大增加识别人工空间簇的假阳性率。此外,我们发现，即使是在高风险真实区域之外的适度不参与也会降低识别真实区域的空间能力。我们提出了一种空间算法来校正潜在的空间结构的非参与，该算法比较了观察到的样本和潜在群体的空间分布。我们证明了其在没有高风险的情况下显着降低假阳性率的能力，并且可以抵抗降低的空间敏感性来检测真正的高风险区域。我们将我们的方法应用于非霍奇金淋巴瘤的病例对照研究。我们的发现表明，应更加关注不参与空间集群研究的潜在影响。
