Challenges in transitional analysis of longitudinal data

纵向研究经常涉及监测动态过程，当科学兴趣在于表征过程中状态之间的转换速率时，过渡模型是最合适的。例子包括慢性疾病的进展、偶发性疾病的发作和消退以及健康促进研究中的行为改变。该建议为具有更复杂类型的关联结构的纵向数据的过渡分析开发了有效的方法。考虑的情况包括集群纵向数据和空间相关的纵向数据。这些类型的数据的过渡性分析的统计挑战在于容纳有效的推论纵向和横截面的关联。这些方法是基于随机效应模型开发的，其中观测数据的关联结构由随机效应的多变量分布的形式控制。设计标准是基于随机效应公式开发的，适用于测试治疗效果。当存在缺失值时，纵向数据的过渡分析会出现独特的挑战。该建议还考虑了在多变量和集群纵向数据设置中处理单调和间歇性缺失数据模式的方法。此外，鉴于大多数过渡方法的主要假设是，该过程遵循具有一定顺序的马尔可夫链，该建议基于使用估计函数的鲁棒方法推导出马尔可夫链顺序的基于分数的测试。 这些项目在以学校为基础的预防研究中发现了实质性的应用，如吸烟行为的变化，双眼视力状况进展的眼科研究，以及BC林业对山松甲虫攻击的研究。

Longitudinal studies frequently involve monitoring dynamic processes, and transitional models are most appropriate when scientific interest lies in characterizing the rates of transitions between states in the processes. Examples include the progression of a chronic disease, the onset and resolution of episodic conditions and behavior change in health promotion studies. This proposal develops efficient methods for the transitional analysis of longitudinal data with more complex types of association structures. Situations considered include clustered longitudinal data and spatially correlated longitudinal data. Statistical challenges in transitional analysis of these kinds of data lie in accommodating both longitudinal and cross-sectional associations for valid inferences. The methods are developed based on random-effects models where the association structure of the observed data is controlled by the form of the multivariate distribution for the random effects. Design criteria are developed based random effect formulations that are suitable for testing treatment effects. Unique challenges arise with transitional analysis of longitudinal data when missing values exist. This proposal also considers methods to handle both monotone and intermittent missing data patterns in the multivariate and clustered longitudinal data setting. In addition, given that the primary assumption in most transitional methods is that the process follows a Markov chain with a certain order, this proposal derives score-based tests for the order of the Markov chain based on the robust approach of using estimating functions.   These projects find substantive applications in school-based prevention studies on changes in smoking behaviors, in ophthalmology studies of the progression of vision conditions for two eyes and, in BC Forestry studies of the attacks of mountain pine beetle.