Developing strategies for handling missing data in time-to-event analyses: Incorporating variable selection, variable transformation and time-varying

制定处理事件时间分析中缺失数据的策略：结合变量选择、变量转换和时变

• 批准号: 1922791
• 金额: --
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1922791
• 关键词: Developing; strategies; handling; missing; data

effects.Missing data is a very common problem in many areas of research, including in survival studies, which are the focus of this research. It can lead to biased estimates of associations between explanatory variables and time to events and a loss of power to detect such associations. Many methods have been used to handle missing data, such as complete-case analysis (ignoring individuals with missing data) and simple imputation approaches, such as mean imputation or regression imputation. However, these methods each are restricted to certain assumptions about the missing data or can suffer from underestimating the degree of uncertainty in estimates. Multiple imputation is a more robust and efficient method of handling missing data, which has become quite popular, by suggesting plausible values to substitute for the missing value. This method, while widely used, has not yet been fully developed to deal with many issues that are faced in practice such as handling time-varying effects of explanatory variables, incorporating flexible transformation of explanatory variables, incorporating variable selection, allowing time-dependent variables, and allowing for measurement error, all of which need to be considered in an analysis. The aim of this project is to develop flexible approaches which incorporate these issues. I will also test the methods using simulation studies, and apply them to real-world data sets. The final product will be an algorithm and worked example with developments made in relevant software to incorporate these new features. Impact: The outcome of this work will be rigorous statistical methods that enable statisticians, epidemiologists and other researchers to handle missing data in their analysis of survival studies. This should help reduce the loss of power to detect associations and lead to less biased estimates, therefore improving the way these studies are performed and improving conclusions. Multiple imputation can be used as an approach to handle other issues, including in adjustment for verification bias, and in observation studies aiming to investigate causal effects using potential outcomes. The work I would do has potential in other areas, therefore broadening the scope of problems it can deal with will benefit these areas. Collaboration: Completing this studentship will provide training and opportunities to collaborate with other researchers. There is the opportunity to become involved with the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative, which is a group of international experts formed with the aim of providing accessible and accurate guidance in the design and analysis of observational studies. This project being relevant to several topic groups such as: Missing data, Selection of variables and functional forms in multivariable analysis and Survival analysis. Another group of interest is the MiDIA group which is concerned with statistical analyses involving missing data and increasing awareness of the problems faced with missing data. Priorities of ESRC: Survival analysis is used in many fields of research, in particular in medical, sociological, economic and public health studies. Many of the data sets available for use, such as routinely collected data, are subject to missing data. The methods developed in this project are therefore expected to be of importance for several priority areas for the ESRC. Training and Skills: Conducting a literature review will improve research strategy skills and knowledge of methods. ESRC Core skills training session: improving networking and presentation skills as well as the ability to express and clarify ideas. Attendance at courses involving missing data, simulation studies, flexible modelling and survival analyses will improve knowledge base. Attendance of a course of the Academy for PhD Training in Statistics will provide further training in modern statistical methods and opportunity to network with fellow students.

缺失数据在许多研究领域都是一个非常常见的问题，包括生存研究，这是本研究的重点。它可能导致对解释变量和事件发生时间之间的关联的有偏估计，并失去检测这种关联的能力。许多方法已被用于处理缺失数据，如完整病例分析（忽略缺失数据的个体）和简单插补方法，如平均插补或回归插补。然而，这些方法都局限于对缺失数据的某些假设，或者可能低估估计的不确定性程度。多重插补是处理缺失数据的一种更稳健和有效的方法，通过建议合理的值来替代缺失值，已变得非常流行。这种方法虽然被广泛使用，但尚未完全发展到处理实践中面临的许多问题，例如处理解释变量的时变效应，纳入解释变量的灵活转换，纳入变量选择，允许时间依赖变量，并允许测量误差，所有这些都需要在分析中考虑。该项目的目的是制定纳入这些问题的灵活办法。我还将使用模拟研究来测试这些方法，并将其应用于真实世界的数据集。最终产品将是一个算法和工作的例子，在相关软件的发展，以纳入这些新功能。影响：这项工作的成果将是严格的统计方法，使统计学家，流行病学家和其他研究人员能够在生存研究分析中处理缺失数据。这应该有助于减少检测关联的能力损失，并导致更少的偏倚估计，从而改善这些研究的执行方式并改进结论。多重插补可用作处理其他问题的方法，包括校正验证偏倚，以及旨在使用潜在结果调查因果效应的观察研究。我所做的工作在其他领域也有潜力，因此扩大它所能处理的问题的范围将有利于这些领域。合作：完成这个学生将提供培训和机会，与其他研究人员合作。有机会参与STRATOS（STRengthening Analytical Thinking for Observational Studies）倡议，该倡议是一个由国际专家组成的小组，旨在为观察性研究的设计和分析提供方便和准确的指导。该项目涉及多个主题组，如：缺失数据，多变量分析和生存分析中变量和函数形式的选择。另一个感兴趣的小组是MiDIA小组，该小组关注涉及缺失数据的统计分析，并提高对缺失数据所面临问题的认识。ESRC的优先事项：生存分析用于许多研究领域，特别是医学，社会学，经济和公共卫生研究。许多可供使用的数据集，如例行收集的数据，都有数据缺失的情况。因此，预计本项目中开发的方法对ESRC的几个优先领域具有重要意义。培训和技能：进行文献综述将提高研究策略技能和方法知识。ESRC核心技能培训课程：提高网络和演讲技巧以及表达和澄清想法的能力。参加涉及缺失数据、模拟研究、灵活建模和生存分析的课程将提高知识基础。参加统计博士培训学院的课程将提供现代统计方法的进一步培训和与同学建立联系的机会。