Design and Analysis of Multiphase Studies Featuring Incomplete Data

动态过程依赖于响应的两阶段设计*我将在大型队列中开发选择个体进行生物标记物子研究的创新方法，旨在研究遗传和血清生物标记物与纵向反应之间的关系，这可能涉及重复测量和事件发生时间数据。第一阶段样本包括收集纵向或生活史数据以及储存在生物库中的标本。第二阶段的样本由一组个人定义，储存的样本被化验，然后可用于分析。第二阶段样本的最优选择算法将基于大样本理论，根据纵向分析的框架得到不同的选择模型(即边际分析、分层混合效应模型、过渡模型)。还将利用似然函数和加权估计函数对病例队列设计的数据进行二次分析，以纠正有偏差的样本。病例队列和嵌套病例对照设计将被推广，以处理更复杂的多状态疾病过程。*有消耗性的队列追踪研究的有效设计*我将为追踪研究的有效设计开发框架，旨在收集队列研究中关于疾病和停药过程的辅助数据。这些数据有助于修正由Mnar数据引起的偏差，并提高效率。分析框架包括疾病-死亡模型，其中关注的是共病或疾病并发症的发展，退行性疾病的进展模型，以及生存模型。辅助协变量数据和逆概率权重的使用将确保有效的推断。如果个人不同意被追踪，那么没有回应的额外权重将被纳入。此外，被追踪的个体可能只提供部分或替代反应，在这种情况下，适应不完整或错误测量反应的方法将值得开发。*辅助数据的家庭研究设计和分析*可利用遗传、表型(例如发病年龄)和辅助数据来开发有效的选择模型，以便从疾病登记中选择先证者纳入家庭研究。我将开发样本选择模型，其中包括这些信息，以及可能的代孕摘要先证者报告的家庭成员数据。家庭研究中使用的有偏见的抽样方案意味着关于疾病发病时间的信息很少。我将探索辅助数据在基因关联得分测试开发中的作用。虽然已经开展了分析家庭数据以获得二元和事件发生时间结果的工作，但在更复杂的多阶段疾病过程中对家庭内依赖关系进行建模的工作还很少；我将重点讨论这一点。

Response-Dependent Two-Phase Designs for Dynamic Processes***I will develop innovative ways of selecting individuals for biomarker sub-studies in large cohorts aimed at examining the relationship between genetic and serum biomarkers to longitudinal responses which may involve repeated measures and time-to-event data. Phase I samples involve the collection of longitudinal or life history data along with specimens stored in biobanks. Phase II samples are defined by the set of individuals for whom the stored specimens are assayed and can then be used in analyses. Algorithms for the optimal selection of Phase II samples will be developed based on large sample theory with different selection models obtained depending on the framework for the longitudinal analysis (i.e. marginal analyses, hierarchical mixed effect models, transition models). Methods for the secondary analysis of data from case-cohort designs will also be developed using likelihood and weighted estimating functions to correct for biased samples. The case-cohort and nested case-control designs will be generalized to deal with more complex multi-state disease processes.******Efficient Design of Tracing Studies for Cohorts with Attrition***I will develop frameworks for the efficient design of tracing studies geared toward the collection of auxiliary data on the disease and withdrawal process in cohort studies. These data facilitate correction for biases arising from data which are MNAR and enhance efficiency. Frameworks for analysis include illness-death models where interest lies in the development of co-morbidities or disease complications, progressive models for degenerative conditions, and survival models. Use of auxiliary covariate data and inverse probability weights will ensure valid inference. If individuals do not consent to be traced then additional weights for non-response will be incorporated. Moreover traced individuals may only provide partial or surrogate responses, in which case methods accommodating incomplete or mismeasured responses will warrant development.******Design and Analysis of Family Studies with Auxiliary Data***Genetic, phenotype (e.g. age of onset) and auxiliary data can be exploited to develop efficient selection models for choosing probands from disease registries for inclusion in family studies. I will develop sample selection models which incorporate such information, as well as possible surrogate summary proband-reported data on family members. The biased sample scheme employed in family studies means there is little information on disease onset times. I will explore the utility of auxiliary data in the development of score tests of genetic associations. While work has been carried out on the analysis of family data for binary and time-to-event outcomes, little has been done for modeling within-family dependencies in more complex multistage disease processes; I will focus on this.