Design and Analysis of Multiphase Studies Featuring Incomplete Data

不完整数据的多相研究的设计和分析

• 批准号: RGPIN-2017-04207
• 负责人: Cook, Richard
• 金额: $ 4.08万
• 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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=648022
• 关键词: Design; Analysis; Multiphase; Studies; Featuring

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.

动态过程的响应依赖性两阶段设计***我将开发创新的方法来选择个体进行大型队列中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。第一阶段样本涉及收集纵向或生活史数据以及生物库中存储的样本。 第二阶段样本是由一组个体定义的，对这些个体对存储的样本进行分析，然后可以将其用于分析。第二阶段样本的优化选择算法将基于大样本理论开发，根据纵向分析框架（即边际分析、分层混合效应模型、过渡模型）获得不同的选择模型。 还将使用可能性和加权估计函数来开发对病例队列设计的数据进行二次分析的方法，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。******具有损耗的队列追踪研究的有效设计***我将开发追踪研究的有效设计框架，旨在收集队列研究中疾病和戒断过程的辅助数据。 这些数据有助于纠正 MNAR 数据产生的偏差并提高效率。 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推断有效。如果个人不同意被追踪，则将纳入未答复的额外权重。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。******利用辅助数据设计和分析家庭研究***可以利用遗传、表型（例如发病年龄）和辅助数据来开发有效的选择模型，从疾病登记处选择先证者纳入家庭研究。我将开发样本选择模型，其中包含此类信息以及可能的替代者总结先证者报告的家庭成员数据。 家庭研究中采用的有偏差的样本方案意味着关于疾病发作时间的信息很少。我将探讨辅助数据在开发遗传关联评分测试中的效用。 虽然已经开展了针对二元和事件发生时间结果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依赖性进行建模方面却做得很少；我将重点关注这一点。