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

缺失数据的临床试验的确定性治疗效果估计方法

• 批准号: 2886293
• 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: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2886293
• 关键词: Methods; deterministic; treatment; effect; estimates

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.

缺少数据通常会使临床试验的统计分析变得复杂。已经开发了几种方法来从试验中获得估计值，这些试验通过对丢失数据与观察到的数据的关系进行假设，以原则上的方式适应这种丢失。但是，这些不可测试的假设无法从观察到的数据中验证。多次插补用于处理缺失的数据，但是，这种方法的缺点是归纳是随机绘制的，因此获得的治疗效果估计值是随机的。根据计算机的随机数种子，这会产生略有不同的答案。对于临床试验的主要分析，这是不受欢迎的，并且确定性方法是非常可取的。该项目旨在研究确定性的单一插补方法，用于处理临床试验中缺失数据的方法，以替代多个插补。该项目将使我能够探索，扩展和开发用于分析缺失数据的新颖方法和模型。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，我将使用基于分析和基于仿真的方法来发展和应用新型统计方法在临床试验分析中应用和应用的技能和经验。这项研究还将使我能够使用现代软件获得统计编程技能。我将在国际领先的生物技术公司的环境中获得协作的经验。我将通过LSHTM和UBEL DTP核心培训计划的博士转移技能计划，包括研究伦理，科学写作和演示技巧，包括研究伦理，科学写作和演示技巧。总体而言，除了开发新的统计方法外，学生奖学金还将导致我的才能和技能的发展，这些方法适用于研究。