Challenges in transitional analysis of longitudinal data

纵向研究经常涉及监测动态过程，当科学兴趣在于描述过程中状态之间的转换速率时，过渡模型是最合适的。例子包括慢性病的进展、发作性疾病的发作和消退以及健康促进研究中的行为改变。本文为具有更复杂关联结构类型的纵向数据的过渡分析提供了有效的方法。考虑的情况包括聚类纵向数据和空间相关纵向数据。在这些类型的数据的过渡分析统计挑战在于适应纵向和横断面的有效推断关联。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形式控制。设计标准是根据随机效应公式制定的，适合测试治疗效果。当缺失值存在时，纵向数据的过渡分析将面临独特的挑战。该建议还考虑了在多变量和聚类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，考虑到大多数过渡方法的主要假设是过程遵循具有一定顺序的马尔可夫链，本文提出了基于使用估计函数的鲁棒方法的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吸烟行为变化的研究、两只眼睛视力状况进展的眼科研究以及BC省林业对山松甲虫攻击的研究中得到了实质性的应用。

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.