A Bayesian Approach to Multistate Hidden Markov Models: Application to Dementia Progression

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 article 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. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.

人类寿命较以往任何时候都要长，随之而来的是人类面临的新并发症和挑战。其中最紧迫的挑战之一便是理解衰老在痴呆症发展过程中的作用。本文以2004年以来对4742名受试者的Mayo Clinic老龄化研究数据为基础，探讨如何利用这些数据来推断衰老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），用以表示与大脑中淀粉样斑块积累和皮质厚度减少相关联的痴呆症进展状态。采用分层贝叶斯方法估计HMM的参数，并使用真正的非齐次无穷小生成矩阵，以及连续生物标志物测量的响应函数不受截点影响。具备这些特征的贝叶斯方法可适用于许多疾病进展模型。此外，本文还阐述了一种纠正延迟入组研究中常见偏差的方法，在此类研究中，部分或全部受试者在基线时未被观察。标准软件无法处理这一关键特性，因此，用于执行以下模型估计的代码已公开发布。与本文一同提交的代码已由副编辑进行可重现性审核，并作为在线补充材料提供。