Statistical Methods for the Design and Analysis of Studies with Incomplete Data

不完整数据研究设计和分析的统计方法

• 批准号: RGPIN-2016-04384
• 负责人: McIsaac, Michael
• 金额: $ 1.46万
• 依托单位: University of Prince Edward Island
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764372
• 关键词: Statistical; Methods; Design; Analysis; Studies

Missing data is a common problem that can be encountered in any research setting due to unanticipated problems in the implementation of a study. Conclusions drawn from such a study are only valid if the missing data are appropriately handled in the statistical analyses. However, statistical methodology is underdeveloped in settings where there are multiple mechanisms contributing to missingness. If interest lies in a composite score that is constructed from several potentially missing variables, then the missing data mechanism will be complex and model misspecification becomes a real concern. I will develop statistical methodologies and provide practical recommendations for effective analysis strategies in these settings.Additionally, missing data can arise by design. When a variable is particularly expensive to assess, it is increasingly common to measure it only for a prespecified validation subset. Optimal selection of the validation subset can lead to great efficiency gains. However, statistical methods for determining such optimal subsamples are underdeveloped in longitudinal studies. I will develop efficient multiphase response-dependent designs to guide selection of individuals for measurement of expensive exposure variables at each wave of data collection in longitudinal cohort studies. These procedures will be adaptive and will exploit information gathered at previous time points. Optimal designs will be derived that result in the most informative completely-observed subsample to maximize the precision of parameter estimates and greatly increase the power of significance tests.This innovative research program will capitalize on my expertise to develop novel statistical methodology for efficient design and analysis of studies with incomplete data and to provide advanced statistical research training for 8 statistics and biostatistics graduate students. This program will provide powerful design tools for researchers conducting longitudinal cohort studies with expensive covariates and provide important insight into appropriate analysis strategies when there may be distinct sources of missingness within a single study. This theoretical statistics research has been motivated by collaborations with applied researchers throughout Canada; the results from this program will provide novel tools to researchers designing studies such as the Canadian Longitudinal Study on Aging, and will offer increased validity to engaged researchers performing analyses of studies involving complex risk factors for both infectious disease (eg, HIV) and non-infectious diseases (eg, heart disease). In short, this research program will benefit many fields of research which struggle with challenges arising from missing data and budgetary constraints in data collection.

数据缺失是任何研究环境中都可能遇到的常见问题，因为在研究实施过程中会出现意想不到的问题。从这种研究中得出的结论只有在统计分析中适当处理缺失数据的情况下才有效。然而，在有多种机制导致遗漏的情况下，统计方法还不够发达。如果感兴趣的是由几个可能缺失的变量构成的合成分数，那么缺失数据的机制将是复杂的，模型错误指定将成为一个真正的问题。我将开发统计方法，并为在这些环境中有效的分析策略提供实用建议。此外，数据丢失可能是由于设计造成的。当一个变量的评估特别昂贵时，只对预先指定的验证子集进行测量的做法越来越普遍。验证子集的最优选择可以带来极大的效率收益。然而，在纵向研究中，确定这种最优子样本的统计方法还不够发达。我将开发有效的多阶段反应依赖设计，以指导在纵向队列研究的每一波数据收集中选择个体来测量昂贵的暴露变量。这些程序将是适应性的，并将利用在以前时间点收集的信息。最优设计将产生信息量最大的完全观察子样本，以最大限度地提高参数估计的精度，并极大地提高显著性检验的能力。这一创新的研究计划将利用我的专业知识，开发新的统计方法，以有效地设计和分析不完整数据的研究，并为8名统计学和生物统计学研究生提供高级统计研究培训。该计划将为研究人员使用昂贵的协变量进行纵向队列研究提供强大的设计工具，并在一项研究中可能存在不同的遗漏来源时，为适当的分析策略提供重要的见解。这项理论统计研究的动力来自于与加拿大各地的应用研究人员的合作；该计划的结果将为设计研究的研究人员提供新的工具，如加拿大老龄化纵向研究，并将为参与研究的研究人员提供更高的有效性，这些研究涉及传染病(如艾滋病毒)和非传染性疾病(如心脏病)的复杂风险因素。简而言之，这一研究计划将使许多领域的研究受益，这些领域正在努力应对数据缺失和数据收集中的预算限制带来的挑战。