Diagnostics for mixture regression models: applications to public health

混合回归模型的诊断：在公共卫生中的应用

• 批准号: nhmrc : 425510
• 负责人: Prof Andy Lee
• 金额: $ 8.55万
• 依托单位: Curtin University
• 依托单位国家: 澳大利亚
• 项目类别: NHMRC Project Grants
• 财政年份: 2007
• 资助国家: 澳大利亚
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://researchdata.edu.au/diagnostics-mixture-regression-public-health/100952
• 关键词: Diagnostics; mixture; regression; models; applications

In many public health studies, finite mixture regression models are often used to analyse data arising from heterogeneous populations. It is important to assess the stability of parameter estimates and the validity of statistical inferences when the underlying assumptions appear to be violated, but appropriate diagnostics are lacking in the literature. This research aims to develop effective diagnostic methods for assessing the adequacy of mixture regression models and the sensitivity of accompanying test statistics. The methodology developed will enable health care professionals to focus on substantive issues and to draw accurate and valid conclusions inferred from correlated and over-dispersed outcomes. In the presence of anomalous observations, the influence diagnostics can provide insights into the source of heterogeneity and the apparent over-dispersion, while accommodating the inherent correlation due to the longitudinal study design or nested data structure. Significance of the research lies in its scientific novelty and the breadth of its practical applications. The benefits to public health will accrue both nationally and internationally. For the empirical studies that motivated and are linked to this research, evaluation of health outcomes has significant implications in the prevention and control of recurrent urinary tract infections, hospital strategic planning, and post-stroke care and rehabilitation management. Moreover, appropriate assessment of a physical activity intervention for older adults is pertinent to falls prevention and reduction of musculoskeletal disorders among sedentary seniors.

在许多公共卫生研究中，有限混合回归模型通常用于分析由异质种群引起的数据。重要的是要评估参数估计值的稳定性和统计推断的有效性，而基本假设似乎违反了，但文献中缺乏适当的诊断。这项研究旨在开发有效的诊断方法，以评估混合回归模型的适当性和随附的测试统计量的灵敏度。开发的方法将使卫生保健专业人员能够专注于实质性问题，并得出从相关和过度分散的结果中推断出的准确和有效的结论。在存在异常观察的情况下，影响诊断可以为异质性和明显的过度分散提供洞察力，同时满足纵向研究设计或嵌套数据结构而固有的相关性。研究的意义在于其科学新颖性及其实际应用的广度。对公共卫生的好处将在国内和国际上带来。对于与这项研究相关的实证研究，对健康结果的评估对预防和控制了复发性尿路感染，医院战略计划以及冲程后护理和康复管理具有重要意义。此外，对老年人的体育活动干预的适当评估与久坐的老年人中肌肉骨骼疾病的跌倒和减少有关。