Methods for deterministic treatment effect estimates for clinical trials with missing data

临床试验的统计分析经常因缺失数据而变得复杂。已经开发了几种方法来从试验中获得估计值，这些试验通过假设缺失数据与观察到的数据之间的关系，以原则性的方式适应这种缺失。然而，这些不可检验的假设无法从观测数据中得到验证。多重插补用于处理缺失数据，然而，该方法的缺点是插补是随机抽取的，因此获得的治疗效应估计值是随机的。这导致根据计算机的随机数种子而略有不同的答案。对于临床试验的主要分析，这是非常不可取的，确定性方法将是更可取的。本项目旨在研究确定性单插补方法，用于处理临床试验中的缺失数据，作为多重插补的替代方法。这个项目将使我能够探索，扩展和开发用于分析缺失数据的新方法和模型。这些方法的应用将提高临床试验中治疗估计的精度，从而有助于产生临床有用的结果。通过这个项目，我将开发新的统计方法的开发和应用的技能和经验，应用于临床试验分析，使用分析和模拟为基础的方法。这项研究也将使我获得使用现代软件的统计编程技能。我将获得在国际领先的生物技术公司环境中协作工作的经验。我将获得可转让的技能，包括研究道德，科学写作，并通过在LSHTM博士可转让技能计划和UBEL DTP核心培训计划演示技巧。总的来说，学生身份将导致我的才华和技能的发展，除了新的统计方法，这将适用于研究的发展。

The statistical analysis of clinical trials is often complicated by missing data. Several methods have been developed for obtaining estimates from trials which accommodate such missingness in a principled way, by making an assumption about how the missing data relate to the observed data. However, these untestable assumptions cannot be verified from the observed data. Multiple imputation is applied to handle missing data, however, a drawback of this methodology is that the imputations are drawn randomly, and hence the treatment effect estimates obtained are random. This results in slightly different answers depending on the computer's random number seed. For the primary analysis of a clinical trial, this is quite undesirable and deterministic methods would be much preferable. This project aims to investigate deterministic single imputation methods for handling missing data in clinical trials, as an alternative to multiple imputation. This project will allow me to explore, expand and develop novel methods and models used for analysis of missing data. Application of these methods will improve the precision of treatment estimates in clinical trials and hence help to generate clinically useful results. Through this project I will develop skills and experience in the development and application of novel statistical methodologies for application in clinical trial analyses, using both analytical and simulation-based approaches. This research will also enable me to gain statistical programming skills using modern software. I will gain experience in working collaboratively within the environment of an internationally leading biotech company. I will gain Transferrable skills, including research ethics, scientific writing, and presentation skills through the Doctoral Transferable Skills Programme at LSHTM and the UBEL DTP Core training program. Overall the studentship will result in the development of my talent and skills in addition to the development of the novel statistical methods which will be applicable in research.