Abstract：
This study develops a model-based index approach called the Generalised Shared Component Model (GSCM) by drawing on the large field of factor models. The proposed fully Bayesian approach accommodates heteroscedastic model error, multiple shared factors and flexible spatial priors. Moreover, unlike previous index approaches, our model provides indices with uncertainty. Focusing on unhealthy behaviors that increase the risk of cancer, the proposed GSCM is used to develop the Area Indices of Behaviors Impacting Cancer product - representing the first area level cancer risk factor index in Australia. This advancement aids in identifying communities with elevated cancer risk, facilitating targeted health interventions.
摘要：
本研究通过借鉴因子模型的大领域，开发了一种基于模型的索引方法，称为广义共享分量模型（GSCM）。所提出的完全贝叶斯方法适应了异方差模型误差，多个共享因素和灵活的空间先验。此外，与以前的索引方法不同，我们的模型提供了具有不确定性的指数。关注增加癌症风险的不健康行为，拟议的GSCM用于开发影响癌症行为的区域指数产品-代表澳大利亚第一个区域级别的癌症危险因素指数。这一进步有助于识别癌症风险升高的社区，促进有针对性的健康干预。
