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

效果。在许多研究领域，包括生存研究中，数据缺失是一个非常普遍的问题，这也是本研究的重点。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差的估计，并失去检测这种关联的能力。有许多方法用于处理缺失数据，例如全案例分析（忽略丢失数据的个体）和简单的归算方法，例如均值归算或回归归算。然而，这些方法都局限于对缺失数据的某些假设，或者可能会低估估计中的不确定性程度。多重插值是处理缺失数据的一种更稳健、更有效的方法，它通过建议合理的值来替代缺失值，已成为一种非常流行的方法。这种方法虽然被广泛使用，但在处理解释变量的时变效应、纳入解释变量的灵活变换、纳入变量选择、允许时变变量、允许测量误差等实践中面临的许多问题时，还没有得到充分的发展，这些都需要在分析中加以考虑。这个项目的目的是制定结合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，并将它们应用于现实世界的数据集。最终的产品将是一个算法和工作的例子，在相关的软件开发，以纳入这些新功能。影响：这项工作的结果将是严谨的统计方法，使统计学家、流行病学家和其他研究人员能够在分析生存研究时处理缺失的数据。这将有助于减少检测关联能力的丧失，减少估计的偏差，从而改进这些研究的开展方式并改进结论。多重归算可以作为处理其他问题的一种方法，包括在验证偏倚的调整中，以及在旨在利用潜在结果调查因果关系的观察研究中。我要做的工作在其他领域也有潜力，因此扩大它可以处理的问题范围将有利于这些领域。合作：完成这项研究将提供与其他研究人员合作的培训和机会。有机会参与STRATOS（加强观察性研究的分析思维）倡议，该倡议是一个国际专家小组，旨在为观察性研究的设计和分析提供方便和准确的指导。该项目涉及几个主题组，如：缺失数据，多变量分析中的变量选择和函数形式以及生存分析。另一个感兴趣的小组是mida小组，它涉及涉及丢失数据的统计分析和对丢失数据所面临的问题的日益认识。生存分析的优先事项：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。许多可用的数据集，例如常规收集的数据，都存在数据缺失的问题。因此，预计本项目制订的方法对经社理事会的若干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改善知识基础。参加统计博士培训学院的课程将提供现代统计方法方面的进一步培训，并有机会与同学建立联系。

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.