贝叶斯疾病制图中的风险估计与边界检测。

IF 1.2 4区 数学
Xueqing Yin, Craig Anderson, Duncan Lee, Gary Napier
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引用次数: 0

摘要

具有空间平滑条件自回归先验分布的贝叶斯层次模型通常用于从面积单位数据估计疾病风险的时空格局。然而,大多数建模方法没有考虑地理相邻区域之间疾病风险阶跃变化的可能边界,这可能导致风险面过于平滑,阻碍高风险区域的检测,并产生疾病风险的偏倚估计。在本文中,我们提出了一种两阶段的方法来共同估计小区域随时间的疾病风险,并检测将具有巨大不同风险的相邻区域分开的边界位置。在第一阶段,我们使用基于图的优化算法来构建一组候选邻域矩阵,这些矩阵代表了疾病数据的一系列可能的边界结构。在第二阶段,将考虑边界的贝叶斯分层时空模型拟合到数据中。在应用于苏格兰大格拉斯哥呼吸系统疾病风险研究之前,通过模拟证明了该方法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Risk estimation and boundary detection in Bayesian disease mapping.

Bayesian hierarchical models with a spatially smooth conditional autoregressive prior distribution are commonly used to estimate the spatio-temporal pattern in disease risk from areal unit data. However, most of the modeling approaches do not take possible boundaries of step changes in disease risk between geographically neighbouring areas into consideration, which may lead to oversmoothing of the risk surfaces, prevent the detection of high-risk areas and yield biased estimation of disease risk. In this paper, we propose a two-stage method to jointly estimate the disease risk in small areas over time and detect the locations of boundaries that separate pairs of neighbouring areas exhibiting vastly different risks. In the first stage, we use a graph-based optimisation algorithm to construct a set of candidate neighbourhood matrices that represent a range of possible boundary structures for the disease data. In the second stage, a Bayesian hierarchical spatio-temporal model that takes the boundaries into account is fitted to the data. The performance of the methodology is evidenced by simulation, before being applied to a study of respiratory disease risk in Greater Glasgow, Scotland.

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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics Mathematics-Statistics and Probability
CiteScore
2.30
自引率
8.30%
发文量
28
期刊介绍: The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.
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