Bayesian spatio-temporal small area modelling: a case study investigating the late-stage melanoma incidence in Texas
提出贝叶斯异质时空逻辑回归模型，利用个体病例数据估计德克萨斯州县级晚期黑色素瘤年发病率，解决数据稀疏和异质性问题，提升癌症监测精度。
Abstract Melanoma is projected to become the second most diagnosed cancer in the United States by 2040, emphasizing the importance of early detection for improving survival outcomes. Accurately estimating late-stage melanoma incidence at granular geographical levels is challenging due to its rarity and data sparsity. Instead of relying on aggregated reported health statistics on melanoma cases, which are commonly used in cancer surveillance research, we utilize the incidence-based individual case database, and propose a Bayesian Heterogeneous Spatio-Temporal Logistic Regression with Nonlinear Demographic Effect (BHSTLR-NDE) model to provide county-level annual estimates of late-stage melanoma incidence in Texas from 2000 to 2018, using data obtained through a request to the Texas Cancer Registry. The BHSTLR-NDE integrates county-level covariates, flexible case-level demographic effects, and a reduced-rank spatial modelling approach to effectively address data sparsity, spatio-temporal non-stationarity, and demographic heterogeneity. The superiority of the proposed BHSTLR-NDE over competing models is demonstrated through extensive simulation studies and an application to melanoma registry data. Results indicate that BHSTLR-NDE effectively borrows information across spatial, temporal, and demographic dimensions, improving estimation accuracy and robustness. These findings highlight the utility of the proposed modelling approach in enhancing cancer surveillance and guiding targeted interventions.