Statistical Methods for Modeling and Design of Life History Studies

生活史研究建模和设计的统计方法

• 批准号: RGPIN-2022-03648
• 负责人: Zeng, Leilei
• 金额: $ 1.97万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765800
• 关键词: Statistical; Methods; Modeling; Design; Life

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.

生活史研究是通过在一段时间内跟踪研究对象并收集关于事件发生和其他结果的信息来进行的。这类研究已被广泛应用于医学、工程学、社会科学和经济学。这项研究计划解决了与生活史研究的建模和设计相关的挑战。具体的研究问题包括：1)多个反复发作的联合分析方法；2)空间相关的生活史数据的建模；3)两阶段纵向试验的适应性设计。在生活史研究中，当涉及几个过程或观察之间存在潜在的集群结构时，就会出现复杂类型的关联结构。例子包括配对或相关的器官系统、多种疾病、基于社区或学校的干预研究。这项提议的主要目标之一是开发统计方法来处理生命历史过程中的关联性和异质性。对于多个重复事件，可以通过在转移强度中引入共享随机效应或通过Copula的方法来建立依赖模型。当感兴趣的是协变量对重复事件过程的边缘特性的影响时，边缘模型更具吸引力，并且过程之间的关联可以使用CUPULA函数来描述。在生活史研究中，当空间聚集在对应于不同子群的种群混合的位置以及它们随时间的演变中都明显时，在生活史研究中就会产生空间相关数据。在混合模型的背景下，对易感人群中失效时间的多变量分布采用Copula函数，对潜在敏感性指标指定边际模型，其中空间关联通过成对的优势比来描述。基于强度的多态模型已越来越多地被用于模拟生命历史过程中的转变。空间随机效应可以被结合到转变强度中，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的复杂关联结构和参数估计方法。自适应设计允许研究本身的特征，例如样本大小或治疗分配的概率，在跟踪过程中根据收集到的数据进行更改。这样的设计已经引起了很多关注，但大多数现有的工作都考虑了简单的情况，即具有立即可观察到的连续或二元响应。在这项研究计划中，将开发用于生命史研究的现代适应性设计，包括生存数据或多状态数据。特别是，纵向试验的两阶段设计中的样本量调整和响应自适应随机化将被研究。