Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.
Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA.
Stat Methods Med Res. 2021 Jan;30(1):35-61. doi: 10.1177/0962280220938975.
Alzheimer's disease is an increasingly prevalent neurological disorder with no effective therapies. Thus, there is a need to characterize the progression of Alzheimer's disease risk in order to preclude its inception in patients. Characterizing Alzheimer's disease risk can be accomplished at the population-level by the space-time modeling of Alzheimer's disease incidence data. In this paper, we develop flexible Bayesian hierarchical models which can borrow risk information from conditions antecedent to Alzheimer's disease, such as mild cognitive impairment, in an effort to better characterize Alzheimer's disease risk over space and time. From an application of these models to real-world Alzheimer's disease and mild cognitive impairment spatiotemporal incidence data, we found that our novel models provided improved model goodness of fit, and via a simulation study, we demonstrated the importance of diagnosing the label-switching problem for our models as well as the importance of model specification in order to best capture the contribution of time in modeling Alzheimer's disease risk.
阿尔茨海默病是一种日益流行的神经退行性疾病，目前尚无有效的治疗方法。因此，有必要描述阿尔茨海默病风险的进展情况，以避免患者发病。通过对阿尔茨海默病发病率数据进行时空建模，可以在人群水平上描述阿尔茨海默病的风险。在本文中，我们开发了灵活的贝叶斯分层模型，这些模型可以从阿尔茨海默病之前的病症（如轻度认知障碍）中获取风险信息，以更好地描述空间和时间上的阿尔茨海默病风险。通过将这些模型应用于现实世界中的阿尔茨海默病和轻度认知障碍的时空发病率数据，我们发现我们的新模型提供了更好的模型拟合优度，并且通过模拟研究，我们证明了诊断我们模型中的标签交换问题以及模型规范的重要性，以便更好地捕捉时间在建模阿尔茨海默病风险方面的贡献。
