Local likelihood disease clustering: development and evaluation
Local likelihood disease clustering: development and evaluation
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DOI：
10.1007/s10651-005-1512-9
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发表时间：
2005-09-01
影响因子：
3.8
通讯作者：
Lawson, AB
中科院分区：
文献类型：
--
作者：
Hossain, M;Lawson, AB
本文阐述了一种基于局部似然（LL）的方法来检测疾病簇。该方法基于估计每个区域的套索距离：在该区域内被认为是聚类的。实现这种方法的一个重要优点是它不需要任何特殊的蒙特卡罗马尔可夫链（MCMC）算法，例如，可逆跳MCMC，这是隐马尔可夫模型方法中必不可少的。另一个优点是，扩展模型以纳入协变量是简单的。我们通过使用东部德国唇癌数据来说明三种方法。通过使用模拟数据，我们与BYM模型[Besag et al.（1991）Annals of the Institute of Statistical Mathematics，43，1-59]和混合模型[Lawson and Clark（2002）Disease Mapping and Risk Assessment for Public Health，Chapman and Hall]进行了比较。我们还对LL模型在不同先验条件下恢复真实相对风险的能力进行了有限的检查。为了检查边缘效应，这在许多疾病映射的空间聚类模型中被忽视，但值得特别注意，因为它缺乏可观察的邻居，我们在这里采用了一种简单的方法来观察边缘区域被省略时相对风险的变化。
This paper illustrates a method based on local likelihood (LL) for detecting disease clusters. The approach is based on estimating a lasso distance for each region: within which regions are considered to be clustered. An important advantage in implementing this approach is that it does not require any special Monte Carlo Markov Chain (MCMC) algorithm, e.g., reversible jump MCMC, which is essential in hidden Markov model approach. Another advantage is that extending the model to incorporate covariates is straightforward. We illustrate three ways of doing this by using Eastern Germany lip cancer data. By using simulated data, we have made a comparison with the BYM model [Besag et al. (1991) Annals of the Institute of Statistical Mathematics, 43, 1-59] and the mixture model [Lawson and Clark (2002) Disease Mapping and Risk Assessment for Public Health, Chapman and Hall]. We also did a limited examination of the ability of the LL model to recover true relative risk under different priors for lasso parameter. In order to check the edge effects, which has been overlooked in many spatial clustering models for disease mapping but deserves special attention as it lacks observable neighbors, we have adapted here a simple approach to observe the changes in relative risks when the edge regions are omitted.