Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.
Stat Methods Med Res. 2012 Dec;21(6):563-83. doi: 10.1177/0962280210391012. Epub 2010 Dec 16.
This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person's membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson's disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson's Disease Rating Scale (UPDRS).
本文探讨了概率分类（即有限混合建模）在给定横截面数据的情况下，用于识别复杂疾病表型的应用。特别是，本文侧重于有限混合建模的标准输出——亚组归属的后验概率，以及如何量化这些概率的不确定性以进行更详细的分析。使用贝叶斯方法，我们描述了这种不确定性的两种实际用途：（i）作为描述一个人属于单个或多个潜在亚组的手段，以及（ii）作为描述通过不在模型估计中包含的以患者为中心的协变量来识别亚组的手段。这些建议的用途在帕金森病（PD）的案例研究中得到了展示，在该研究中，使用统一帕金森病评定量表（UPDRS）中的多个症状来识别潜在亚组。
