Sampling designs and statistical methods for incomplete data analysis

不完全数据分析的抽样设计和统计方法

• 批准号: RGPIN-2014-04904
• 负责人: Yilmaz, Yildiz
• 金额: $ 1.09万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=675645
• 关键词: Sampling; designs; statistical; methods; incomplete

Many studies involve incomplete data which may arise due to the study design under consideration, or missingness by happenstance. For example, budgetary constraints may prevent measuring expensive covariates for all individuals in a cohort which leads us to consider appropriate sampling designs and to have incomplete data as a result of the sampling design. The main goal of this research proposal is to identify efficient sampling designs under such a situation in different data type settings, and develop, evaluate and apply statistical methods for incomplete data analysis. To limit sample size for obtaining values of expensive covariates while giving adequate power for their corresponding association tests, the best solution is to obtain them under a cost-efficient sampling design and to use efficient statistical methods that lead to efficient estimates and powerful association tests. It is known that selecting an informative subset of individuals from an existing cohort based on response-dependent sampling can improve cost-efficiency of studies. We will consider outcome-dependent two-phase sampling designs: in phase one, we have easily measured variables for all individuals in the cohort or in a large random sample from the population, and in phase two, we obtain expensive variables for a subset of individuals selected according to their response variable obtained in phase one. In response-dependent sampling designs, inference based on standard statistical methods, ignoring the selection, may be misleading. **There have been many studies on developing methods to efficiently analyze response-dependent multi-phase sampling designs in the literature. However, there has not been sufficient work on identifying efficient outcome-dependent multi-phase sampling designs. The objective of this proposal is to develop analytic and simulation-based approaches to compare various sampling designs under each proposed method according to the allocation of the phase two samples, the distribution of the expensive covariate and associated effect size, as well as to check the robustness of methods under misspecification of model assumptions. We will consider response-dependent sampling designs under different data type settings. For example, the response variable can be a time-to-event variable, which may not be completely observed but censored for some individuals; or there can be multiple continuous uncensored or time-to-event response variables and the sampling in the second phase may depend on multiple response variables. **This study will be helpful in addressing how to optimally sample subjects to obtain the best power to identify the associations between response variable(s) and expensive covariate(s) for a given sample size, and which method of analysis leads to more powerful association tests under specified modeling assumptions. It will also be helpful to identify sampling designs and statistical methods which are less than optimal but may be more robust to model misspecification. Hence, it is anticipated that we will have a better understanding about cost-efficient sampling designs that will be beneficial in reducing costs of many research studies in Natural Sciences, Social Sciences and Health Sciences, and therefore, be beneficial to the economy of Canada. In addition, statistical methods will be developed under complex modeling assumptions for response-selective problems, which have not been considered deeply in the literature.

许多研究涉及不完整的数据，这可能是由于研究设计所引起的，或者是偶然的缺失。例如，预算限制可能会阻止测量一个队列中所有个体的昂贵协变量，这导致我们考虑适当的抽样设计，并由于抽样设计而获得不完整的数据。本研究计划的主要目标是在不同的数据类型设置下，识别在这种情况下有效的抽样设计，并开发、评估和应用不完整数据分析的统计方法。为了限制获得昂贵协变量值的样本量，同时为其相应的关联检验提供足够的能力，最佳解决方案是在具有成本效益的抽样设计下获得它们，并使用有效的统计方法，从而获得有效的估计和强大的关联检验。众所周知，基于响应相关抽样从现有队列中选择信息丰富的个体子集可以提高研究的成本效率。我们将考虑结果相关的两阶段抽样设计：在第一阶段，我们很容易测量队列中所有个体或来自人群的大型随机样本中的变量，在第二阶段，我们根据第一阶段获得的响应变量为个体子集选择昂贵的变量。在响应相关抽样设计中，基于标准统计方法的推断，忽略了选择，可能会产生误导。**文献中已经有很多关于开发有效分析响应相关多相抽样设计的方法的研究。然而，在确定有效的结果相关多相采样设计方面还没有足够的工作。本提案的目的是开发基于分析和模拟的方法，根据第二阶段样本的分配，昂贵协变量的分布和相关效应大小，比较每种提议方法下的各种抽样设计，以及检查模型假设错误规范下方法的稳健性。我们将考虑不同数据类型设置下的响应相关抽样设计。例如，响应变量可以是一个时间到事件的变量，它可能不会被完全观察到，但对某些个体来说是被删减的；或者可能存在多个连续的未审查或时间到事件响应变量，并且第二阶段的采样可能依赖于多个响应变量。**本研究将有助于解决在给定样本量下，如何优化样本受试者以获得识别响应变量与昂贵协变量之间关联的最佳能力，以及在指定的建模假设下，哪种分析方法会导致更强大的关联检验。它也将有助于识别抽样设计和统计方法，这些方法不是最优的，但可能对模型错误规范更具鲁棒性。因此，预计我们将对具有成本效益的抽样设计有更好的了解，这将有助于降低自然科学、社会科学和卫生科学领域许多研究的成本，从而有利于加拿大的经济。此外，统计方法将在复杂的建模假设下开发，以解决响应选择问题，这在文献中尚未得到深入考虑。