Hierarchical finite mixture modelling of health outcomes: a risk-adjusted random effects approach

在医学和健康研究中，有限混合回归模型已被用于分析来自异质群体的数据。传统上，混合模型的应用主要涉及有限正态混合。最近的计算进步和方法的发展，增强了扩展的方法，非正常的有限混合物，如广义线性模型和重叠阶段的故障时间数据的生存分析的背景下，在有限混合物的离散响应的建模。然而，由于分层研究设计或数据收集程序，固有的相关结构和/或聚类效应可能会导致额外的变化和违反独立性假设，导致虚假的关联和误导性的推断基于有限混合模型。该项目旨在通过将随机效应纳入有限混合回归模型，提出一种统一的方法来适应观测的异质性和依赖性。新的方法将提供一个综合框架来分析异质和相关的健康结果。三个实证研究被认为是，即职业伤害减少干预措施，住院时间建模，并分析心脏手术后的患者生存时间的评价。生物科学的长期利益是从医学和健康研究中得出准确和有效的结论，以及正确识别高风险亚组。对于该项目的三个应用领域，改进的分析将特别能够评估参与式人机工程学干预，评估医院效率和影响住院时间的因素，并分别确定术前和术后处方治疗的有效性。

In medical and health studies, finite mixture regression models have been used to analyze data arising from heterogeneous populations. Traditionally, the application of mixture models is mainly concerned with finite normal mixtures. Recent computational advances and methodological developments have enhanced the extension of the method to non-normal finite mixtures, such as the modelling of discrete responses in finite mixture of generalized linear models and overlapping phases of failure time data in the context of survival analysis. However, due to the hierarchical study design or the data collection procedure, the inherent correlation structure and-or clustering effects present may contribute to extra variations and violation of the independence assumption, resulting in spurious associations and misleading inferences based on the finite mixture model. This project aims to present a unified approach to accommodate both heterogeneity and dependency of observations, by incorporating random effects into finite mixture regression models. The new methodology will provide an integrated framework to analyze heterogeneous and correlated health outcomes. Three empirical studies are considered, namely, evaluation of an occupational injury reduction intervention, length of hospital stay modeling, and analysis of survival times of patients after cardiac surgery. The long term benefits to bioscience are accurate and valid conclusions inferred from medical and health studies, as well as the correct identification of high-risk subgroups. For the three application areas of this project, the improved analyses will specifically enable the evaluation of a participatory ergonomics intervention, the assessment of hospital efficiency and factors influencing length of hospitalization, and the determination of effectiveness of treatments prescribed pre- and post- operation, respectively.