Assunção R, Costa M, Tavares A, Ferreira S
Departamento de Estatística, Universidade Federal de Minas Gerais, 31270-901, Belo Horizonte, MG Brazil.
Stat Med. 2006 Mar 15;25(5):723-42. doi: 10.1002/sim.2411.
Disease cluster detection and evaluation have commonly used spatial statistics methods that scan the map with a fixed circular window to locate candidate clusters. Recently, there has been interest in searching for clusters with arbitrary shape. The circular scan test retains high power of detecting a cluster, but does not necessarily identify the exact regions contained in a non-circular cluster particularly well. We propose, implement and evaluate a new procedure that is fast and produces clusters estimates of arbitrary shape in a rich class of possible cluster candidates. We showed that our methods contain the so-called upper level set method as a particular case. We present a power study of our method and, among other results, the main conclusion is that the likelihood-based arbitrarily shaped scan method is not appropriate to find a cluster estimate. When the parameter space includes the set of all possible spatial clusters in a map, a large and discrete parameter space, maximum likely cluster estimates tend to overestimate the true cluster by a large extent. This calls for a new approach different from the maximum likelihood method for this important public health problem.
疾病聚集性检测与评估通常采用空间统计方法，即用固定的圆形窗口扫描地图以定位候选聚集区。最近，人们开始关注寻找任意形状的聚集区。圆形扫描检验在检测聚集区方面具有较高的功效，但不一定能很好地识别非圆形聚集中的确切区域。我们提出、实施并评估了一种新方法，该方法速度快，能在丰富的可能候选聚集区类别中生成任意形状的聚集区估计。我们表明，我们的方法在特定情况下包含所谓的上水平集方法。我们对我们的方法进行了功效研究，主要结论是，基于似然的任意形状扫描方法不适用于寻找聚集区估计。当参数空间包括地图中所有可能的空间聚集区集合（一个大的离散参数空间）时，最大似然聚集区估计往往会在很大程度上高估真实聚集区。对于这个重要的公共卫生问题，这就需要一种不同于最大似然法的新方法。
