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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=588091
• 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，这可能是由于正在考虑的研究设计，或偶然丢失。例如，预算限制可能会阻止测量昂贵的协变量为所有个人在一个队列中，导致我们考虑适当的抽样设计，并有不完整的数据作为抽样设计的结果。本研究提案的主要目标是在不同数据类型设置的情况下确定有效的抽样设计，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功效，最好的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，基于响应依赖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益。我们将考虑结果依赖的两阶段抽样设计：在第一阶段，我们很容易测量队列中所有个体的变量或从人群中随机抽取的大样本，在第二阶段，我们根据第一阶段获得的响应变量选择一个个体子集，获得昂贵的变量。在响应依赖抽样设计中，基于标准统计方法的推断，忽略选择，可能会产生误导。 在文献中，已经有许多关于开发方法以有效地分析响应依赖的多相采样设计的研究。然而，还没有足够的工作，以确定有效的结果依赖多阶段抽样设计。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量，比较每种拟议方法下的各种抽样设计，并检查模型假设错误情况下方法的稳健性。我们将考虑不同数据类型设置下的响应依赖抽样设计。例如，响应变量可以是事件发生时间变量，其可能未被完全观察到，但对某些个体进行了删失;或者可以存在多个连续的未删失或事件发生时间响应变量，并且第二阶段中的采样可能取决于多个响应变量。 本研究将有助于解决如何最佳地对受试者进行抽样，以获得最佳功效来识别给定样本量的响应变量与昂贵协变量之间的关联，以及在指定建模假设下，哪种分析方法会导致更强大的关联检验。它也将有助于确定抽样设计和统计方法，这些方法不是最佳的，但可能对模型误设更稳健。因此，预计我们将更好地了解具有成本效益的抽样设计，这将有利于降低自然科学，社会科学和健康科学中许多研究的成本，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响应选择性问题，这在文献中没有被深入考虑。