Statistical Methods for Modeling and Design of Life History Studies

生活史研究是通过在一段时间内跟踪研究对象并收集有关事件发生和其他结果的信息来进行的。这些研究已广泛应用于医学、工程、社会科学和经济学。这个研究项目解决了与生命史研究建模和设计相关的挑战。具体研究问题包括：1)多次反复发作的联合分析方法；2)建立空间相关生活史数据模型；3)两期纵向试验的自适应设计。复杂类型的关联结构出现在生命史研究中，当涉及多个过程或在观察中存在潜在的集群结构时。例子包括成对或相关的器官系统、多种类型的疾病、社区或学校干预研究。本提案的主要目标之一是发展统计方法来处理生活史过程中的关联和异质性。对于多次反复发作，可以通过在过渡强度中引入共享随机效应或通过Copula建立依赖模型。当兴趣在于协变量对循环事件过程的边际特性的影响时，边际模型更有吸引力，过程之间的关联可以使用cupula函数来表示。空间相关数据出现在生命史研究中，当空间聚类在不同亚群对应的种群混合物的位置及其随时间的演变中都很明显时。在混合模型中，易感群体失效次数的多变量分布采用copula函数，潜在易感指标采用边际模型，两两比值比表征其空间关联。基于强度的多状态模型越来越多地用于生活史过程的过渡建模。空间随机效应可以纳入过渡强度，以解释由于环境风险因素导致的疾病进展的相关性。与这些模型相关的挑战包括动态过程之间复杂的关联结构和参数估计方法。适应性设计允许研究本身的特征，例如样本量或治疗分配的概率，在跟踪过程中根据收集的数据发生变化。这样的设计已经引起了很多关注，但大多数现有的工作考虑的是具有可立即观察到的连续或二元响应的简单情况。现代适应性设计与生存或多状态数据的生活史研究将在本研究计划中发展。特别是，在纵向试验的两阶段设计中，样本量调整和响应自适应随机化将被研究。

Life history studies are conducted by following study subjects over some period of time and collecting information about the occurrence of events and other outcomes. Such studies have been widely used in medicine, engineering, social sciences and economics. This research program addresses challenges associated with modeling and designs for life history studies. The specific research problems include: 1) methods for joint analysis of multiple recurrent episodes; 2) modelling spatially correlated life history data; and 3) adaptive designs for two-stage longitudinal trials.   Complex types of association structure arise in life history studies when several processes are involved or underlying cluster structures exists among observations. Examples include paired or related organ systems, multiple types of diseases, community or school based intervention studies. One of the main objectives of this proposal is to develop statistical methods to deal with association and heterogeneity in life history processes. For multiple recurrent episodes, dependence model can be formulated by introducing shared random effects in transition intensities or via Copula's. When interest lies in covariate effects on marginal properties of recurrent event processes, marginal models are more appealing and associations among processes can be formulated using cupula functions. Spatially correlated data arise in life history studies when spatial clustering is evident both in the placement of population mixtures corresponding to different sub-groups and in their evolution over time. In the context of mixture models, copula functions are adopted for the multivariate distribution of failure times in susceptible population, marginal models are specified for the latent susceptibility indicators where the spatial associations are characterized via pairwise odds ratios. Intensity based multistate models have been increasingly used for modelling transitions in life history processes. Spatial random effects can be incorporated in the transition intensities to account for the correlation in disease progression due to environmental risk factors. The challenges associated with these models include complex association structures between dynamic processes and methods for parameter estimation.  Adaptive designs allow characteristics of the study itself, for example sample size or probability of treatment assignment, to change during the trail in response to data being collected. Such designs have drew a lot of attention but most of the exiting work consider the simple situation with a continuous or binary response that is immediately observable. Modern adaptive designs for life history studies with survival or multistate data will be developed in this research program. In particular, sample size adjustment and response adaptive randomization in a two-stage design for longitudinal trials will be investigated.