Greco Fedele P, Trivisano Carlo
Department of Statistics P. Fortunati, University of Bologna, Italy.
Stat Med. 2009 May 30;28(12):1707-24. doi: 10.1002/sim.3577.
Disease mapping studies have been widely performed at univariate level, that is considering only one disease in the estimated models. Nonetheless, simultaneous modelling of different diseases can be a valuable tool both from the epidemiological and from the statistical point of view. In this paper we propose a model for multivariate disease mapping that generalizes the univariate conditional auto-regressive distribution. The proposed model is proven to be an effective alternative to existing multivariate models, mainly because it overcome some restrictive hypotheses underlying models previously proposed in this context. Model performances are checked via a simulation study and via application to a case study.
疾病映射研究已在单变量层面广泛开展，即在估计模型中仅考虑一种疾病。尽管如此，从流行病学和统计学角度来看，对不同疾病进行同时建模可能是一种有价值的工具。在本文中，我们提出了一种用于多变量疾病映射的模型，该模型推广了单变量条件自回归分布。所提出的模型被证明是现有多变量模型的有效替代方案，主要是因为它克服了此前在此背景下提出的模型所基于的一些限制性假设。通过模拟研究和应用于一个案例研究来检验模型性能。
