Design and Analysis of Multiphase Studies Featuring Incomplete Data

***我将开发创新的方法来选择个体进行大型队列的生物标志物子研究，旨在检查遗传和血清生物标志物与纵向反应之间的关系，这可能涉及重复测量和事件时间数据。第一阶段的样本包括收集纵向或生活史数据，以及储存在生物库中的样本。第二阶段的样品是由一组个体来定义的，这些个体所储存的样品被分析，然后可以用于分析。第二阶段样本的优化选择算法将基于大样本理论，并根据纵向分析的框架（即边际分析、层次混合效应模型、过渡模型）获得不同的选择模型。病例队列设计数据的二次分析方法也将使用似然和加权估计函数来纠正有偏样本。病例队列和嵌套病例对照设计将被推广到处理更复杂的多状态疾病过程。******减少队列追踪研究的有效设计***我将开发有效设计追踪研究的框架，旨在收集队列研究中疾病和退出过程的辅助数据。这些数据有助于纠正由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（关注的是合并症或疾病并发症的发展）、退行性疾病的渐进模型和生存模型。使用辅助协变量数据和逆概率权重将确保有效的推理。如果个人不同意跟踪，则将纳入未响应的额外权重。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。******带有辅助数据的家庭研究的设计和分析***可以利用遗传、表型（如发病年龄）和辅助数据来开发有效的选择模型，以便从疾病登记处选择先证以纳入家庭研究。我将开发包含这些信息的样本选择模型，以及可能的替代摘要先证者报告的家庭成员数据。在家庭研究中采用的有偏样本方案意味着很少有关于疾病发病时间的信息。我将探讨在遗传关联的得分测试的发展辅助数据的效用。虽然已经开展了关于二元结果和事件发生时间的家庭数据分析工作，但在更复杂的多阶段疾病过程中，对家庭内部依赖关系进行建模的工作很少；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.