多状态隐马尔可夫模型的贝叶斯方法：在痴呆进展中的应用
A Bayesian Approach to Multi-State Hidden Markov Models: Application to Dementia Progression
摘要：
人们的寿命比以往任何时候都长，这给人类带来了新的复杂性和挑战。其中最紧迫的挑战是了解衰老在痴呆症发展中的作用。这篇论文的动机是梅奥诊所自2004年以来对4742名受试者的老龄化数据研究，以及它如何被用来推断老龄化在痴呆症发展中的作用。我们构建了一个隐马尔可夫模型（HMM）来表示痴呆症的进展与淀粉样蛋白斑块在大脑中的积累，和皮质厚度的损失。采用分层贝叶斯方法来估计具有真正时间非齐次无穷小生成矩阵的HMM的参数，并且连续值生物标志物测量的响应函数是切割点不可知的。具有这些特征的贝叶斯方法在许多疾病进展模型中可能是有用的。此外，说明了一种方法，用于纠正延迟入组研究中的常见偏倚，其中一些或所有受试者在基线时未观察到。标准软件无法解释这一关键特征，因此可以在线获得执行下文所述模型估计的代码。
英文摘要：
People are living longer than ever before, and with this arises new complications and challenges for humanity. Among the most pressing of these challenges is of understanding the role of aging in the development of dementia. This paper is motivated by the Mayo Clinic Study of Aging data for 4742 subjects since 2004, and how it can be used to draw inference on the role of aging in the development of dementia. We construct a hidden Markov model (HMM) to represent progression of dementia from states associated with the buildup of amyloid plaque in the brain, and the loss of cortical thickness. A hierarchical Bayesian approach is taken to estimate the parameters of the HMM with a truly time-inhomogeneous infinitesimal generator matrix, and response functions of the continuous-valued biomarker measurements are cut-point agnostic. A Bayesian approach with these features could be useful in many disease progression models. Additionally, an approach is illustrated for correcting a common bias in delayed enrollment studies, in which some or all subjects are not observed at baseline. Standard software is incapable of accounting for this critical feature, so code to perform the estimation of the model described below is made available online.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
