Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Int J Public Health. 2020 Jun;65(5):673-682. doi: 10.1007/s00038-020-01384-5. Epub 2020 May 24.
This study aimed to review the types and applications of fully Bayesian (FB) spatial-temporal models and covariates used to study cancer incidence and mortality.
This systematic review searched articles published within Medline, Embase, Web-of-Science and Google Scholar between 2014 and 2018.
A total of 38 studies were included in our study. All studies applied Bayesian spatial-temporal models to explore spatial patterns over time, and over half assessed the association with risk factors. Studies used different modelling approaches and prior distributions for spatial, temporal and spatial-temporal interaction effects depending on the nature of data, outcomes and applications. The most common Bayesian spatial-temporal model was a generalized linear mixed model. These models adjusted for covariates at the patient, area or temporal level, and through standardization.
Few studies (4) modelled patient-level clinical characteristics (11%), and the applications of an FB approach in the forecasting of spatial-temporally aligned cancer data were limited. This review highlighted the need for Bayesian spatial-temporal models to incorporate patient-level prognostic characteristics through the multi-level framework and forecast future cancer incidence and outcomes for cancer prevention and control strategies.
本研究旨在回顾用于研究癌症发病率和死亡率的完全贝叶斯（FB）时空模型和协变量的类型和应用。
本系统评价检索了 2014 年至 2018 年期间在 Medline、Embase、Web-of-Science 和 Google Scholar 中发表的文章。
共有 38 项研究纳入本研究。所有研究均应用贝叶斯时空模型来探索随时间变化的空间模式，超过一半的研究评估了与风险因素的相关性。根据数据、结局和应用的性质，研究采用了不同的建模方法和先验分布来处理空间、时间和时空交互效应。最常见的贝叶斯时空模型是广义线性混合模型。这些模型通过标准化，在患者、地区或时间水平上对协变量进行了调整。
少数研究（4 项）对患者水平的临床特征进行了建模（11%），且 FB 方法在时空对齐的癌症数据预测方面的应用有限。本综述强调了需要通过多层次框架将患者水平的预后特征纳入贝叶斯时空模型，并预测未来癌症的发病率和结局，以制定癌症预防和控制策略。
