MacNab Ying C, Gustafson Paul
Division of Epidemiology and Biostatistics, Department of Health Care and Epidemiology, University of British Columbia, Vancouver, BC, Canada.
Stat Med. 2007 Oct 30;26(24):4455-74. doi: 10.1002/sim.2868.
In the context of Bayesian disease mapping, recent literature presents generalized linear mixed models that engender spatial smoothing. The methods assume spatially varying random effects as a route to partially pooling data and 'borrowing strength' in small-area estimation. When spatiotemporal disease rates are available for sequential risk mapping of several time periods, the 'smoothing' issue may be explored by considering spatial smoothing, temporal smoothing and spatiotemporal interaction. In this paper, these considerations are motivated and explored through development of a Bayesian semiparametric disease mapping model framework which facilitates temporal smoothing of rates and relative risks via regression B-splines with mixed-effect representation of coefficients. Specifically, we develop spatial priors such as multivariate Gaussian Markov random fields and non-spatial priors such as unstructured multivariate Gaussian distributions and illustrate how time trends in small-area relative risks may be explored by splines which vary in either a spatially structured or unstructured manner. In particular, we show that with suitable prior specifications for the random effects ensemble, small-area relative risk trends may be fit by 'spatially varying' or randomly varying B-splines. A recently developed Bayesian hierarchical model selection criterion, the deviance information criterion, is used to assess the trade-off between goodness-of-fit and smoothness and to select the number of knots. The methodological development aims to provide reliable information about the patterns (both over space and time) of disease risks and to quantify uncertainty. The study offers a disease and health outcome surveillance methodology for flexible and efficient exploration and assessment of emerging risk trends and clustering. The methods are motivated and illustrated through a Bayesian analysis of adverse medical events (also known as iatrogenic injuries) among hospitalized elderly patients in British Columbia, Canada.
在贝叶斯疾病地图绘制的背景下，近期文献提出了可实现空间平滑的广义线性混合模型。这些方法将空间变化的随机效应视为在小区域估计中进行部分数据合并和“借用强度”的途径。当有多个时间段的时空疾病发病率可用于连续风险地图绘制时，可以通过考虑空间平滑、时间平滑和时空交互来探讨“平滑”问题。在本文中，通过开发一个贝叶斯半参数疾病地图绘制模型框架来推动并探索这些考虑因素，该框架通过具有系数混合效应表示的回归B样条促进发病率和相对风险的时间平滑。具体而言，我们开发了诸如多元高斯马尔可夫随机场之类的空间先验和诸如无结构多元高斯分布之类的非空间先验，并说明了如何通过以空间结构化或非结构化方式变化的样条来探索小区域相对风险的时间趋势。特别是，我们表明，通过对随机效应集合进行适当的先验设定，小区域相对风险趋势可以通过“空间变化”或随机变化的B样条来拟合。一种最近开发的贝叶斯层次模型选择标准——偏差信息准则，用于评估拟合优度与平滑度之间的权衡并选择节点数量。方法学的发展旨在提供有关疾病风险模式（包括空间和时间模式）的可靠信息并量化不确定性。该研究提供了一种疾病和健康结果监测方法，用于灵活高效地探索和评估新出现的风险趋势及聚集情况。通过对加拿大不列颠哥伦比亚省住院老年患者的不良医疗事件（也称为医源性损伤）进行贝叶斯分析，激发并阐述了这些方法。
