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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709279
• 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.

缺失数据是一个常见的问题，可以在任何研究环境中遇到的，由于在实施研究的意外问题。只有在统计分析中适当处理缺失数据时，从此类研究中得出的结论才有效。然而，在存在多种导致失踪的机制的情况下，统计方法还不发达。如果兴趣在于由几个潜在缺失变量构建的综合得分，那么缺失数据机制将是复杂的，模型错误指定成为真实的问题。我将开发统计方法，并为这些环境中的有效分析策略提供实用建议。 此外，缺失数据可能是设计造成的。当一个变量的评估成本特别高时，越来越普遍的做法是只对一个预先指定的验证子集进行测量。验证子集的最佳选择可以导致极大的效率增益。然而，统计方法来确定这样的最佳子样本是欠发达的纵向研究。我将开发有效的多相响应依赖设计，以指导在纵向队列研究的每一波数据收集中选择用于测量昂贵暴露变量的个体。这些程序将是自适应的，并将利用在先前时间点收集的信息。将推导出最佳设计，以获得信息量最大的完全观察子样本，从而最大限度地提高参数估计值的精度，并大大提高显著性检验的功效。 这个创新的研究项目将利用我的专业知识，开发新的统计方法，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先进的统计研究培训。该计划将为研究人员提供强大的设计工具，进行纵向队列研究，昂贵的协变量，并提供重要的洞察适当的分析策略时，可能有不同的来源，在一个单一的研究缺失。这项理论统计研究的动机是与加拿大各地的应用研究人员合作;该计划的结果将为研究人员设计研究提供新的工具，如加拿大老龄化纵向研究，并将为从事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，这项研究计划将使许多研究领域受益，这些领域正在努力应对数据收集中缺失数据和预算限制所带来的挑战。