Aregay Mehreteab, Lawson Andrew B, Faes Christel, Kirby Russell S
1 Department of Public Heath Sciences, Division of Biostatistics and Bioinformatics, MUSC, Charleston, USA.
2 Interuniversity Institute for Biostatistics, statistical Bioinformatics, Hasselt University, Hasselt, Belgium.
Stat Methods Med Res. 2017 Dec;26(6):2726-2742. doi: 10.1177/0962280215607546. Epub 2015 Sep 29.
In disease mapping, a scale effect due to an aggregation of data from a finer resolution level to a coarser level is a common phenomenon. This article addresses this issue using a hierarchical Bayesian modeling framework. We propose four different multiscale models. The first two models use a shared random effect that the finer level inherits from the coarser level. The third model assumes two independent convolution models at the finer and coarser levels. The fourth model applies a convolution model at the finer level, but the relative risk at the coarser level is obtained by aggregating the estimates at the finer level. We compare the models using the deviance information criterion (DIC) and Watanabe-Akaike information criterion (WAIC) that are applied to real and simulated data. The results indicate that the models with shared random effects outperform the other models on a range of criteria.
在疾病地图绘制中，由于数据从较精细分辨率级别聚合到较粗糙级别而产生的尺度效应是一种常见现象。本文使用分层贝叶斯建模框架来解决这个问题。我们提出了四种不同的多尺度模型。前两种模型使用一种共享随机效应，即较精细级别继承自较粗糙级别。第三种模型假设在较精细和较粗糙级别有两个独立的卷积模型。第四种模型在较精细级别应用卷积模型，但较粗糙级别的相对风险是通过聚合较精细级别上的估计值获得的。我们使用应用于真实数据和模拟数据的偏差信息准则（DIC）和渡边赤池信息准则（WAIC）来比较这些模型。结果表明，具有共享随机效应的模型在一系列标准上优于其他模型。
