Health Data Science CDT

健康数据科学 CDT

• 批准号: 2876277
• 金额: --
• 依托单位: University of Oxford
• 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-2876277
• 关键词: Health; Data; Science; CDT

Research in the medical sciences has grown substantially, covering a wide range of topics and using a range of study designs. Alongside the proliferation of clinical trials, numerous biobanks have recently been established, offering access to detailed observational datasets on thousands of people. However, despite the availability of these extensive datasets, challenges remain in how to best use these new resources.Often studies on similar topics will be done in different contexts and using differing study designs. Without sophisticated methods to integrate these sources of evidence, the increase in new studies can lead to more uncertainty rather than better inferences. Study designs have different strengths, weaknesses and sources of bias, and without good analysis methods this can present a confusing picture. Improved statistical methods would allow us to make full use of new data resources.Our proposal has three main aims: to improve methods for combining observational and randomised data, to better understand the theoretical limits of this process and to use principles from Bayesian Experimental Design to design more informative experiments.We will describe these aims in more detail below, and highlight areas where we hope to develop new methods. This project falls within the EPSRC strategic priority of transforming health and healthcare.There has recently been growing interest in developing methods to combine evidence from randomised control trials with observational data. Such methods aim to leverage the very precise but often biased estimates from observational data, and combine them with potentially noisy but internally consistent estimates from randomised trials. In the course of our mini-project preceding this PhD, we proposed a new method, combining aspects of two previously proposed solutions to this problem. This method aims to first adjust for the impact of hidden confounders in our randomised dataset, and then incorporate the adjusted data depending on how well this adjustment has worked. We achieved some encouraging results in early simulations but aim to further improve this method and test it in a wider range of contexts, including using semi-synthetic and real-world data. There is also potential to combine other methods in new ways to improve inference.As well as developing further methods to improve inference, our second aim will be to improve on the current theoretical basis of this field, where methods often rely on substantial assumptions. A starting point in this direction would be investigating whether it is possible to guarantee an improvement in CATE estimation by adding an arbitrarily biased observational dataset to a randomised dataset without further assumptions. Alternatively, can it be proven that this is impossible? Either of these findings would be a novel contribution to the field.Finally, as well as improving inference using existing data, we would like to explore whether it is possible to collect data more efficiently to maximise our expected information gain using Bayesian Experimental Design. There is a range of existing research in this area, althoughapplication of these methods has lagged somewhat behind the statistical literature. Initially, we would like to explore whether it is possible to design more efficient randomised control trials which can achieve similar precision in CATE estimates with fewer participants. Eventually we hope that we might be able to use these methods to improve experimental design for causal discovery.The rapid growth in medical research presents both opportunities and challenges. To fully utilize diverse data sources, advanced statistical methods are needed. Our aims to improve data combination techniques, understand theoretical limits, and use Bayesian Experimental Design will help draw more robust conclusions and advance the field, supporting the ESPRC's strategic healthcare priorities.

医学方面的研究有了很大的增长，涵盖了广泛的主题，并使用了一系列的研究设计。随着临床试验的激增，最近建立了许多生物库，提供对数千人的详细观察数据集。然而，尽管有这些广泛的数据集可用，但如何最好地利用这些新资源仍然存在挑战。关于类似主题的研究通常会在不同的背景下进行，并使用不同的研究设计。如果没有复杂的方法来整合这些证据来源，新研究的增加可能会导致更多的不确定性，而不是更好的推断。研究设计有不同的优点、缺点和偏差来源，如果没有好的分析方法，这可能会呈现出一幅令人困惑的图景。改进的统计方法将使我们能够充分利用新的数据资源。我们的建议有三个主要目标：改进结合观测数据和随机数据的方法，更好地理解这一过程的理论限制，并使用贝叶斯实验设计的原理来设计更有信息量的实验。我们将在下面更详细地描述这些目标，并强调我们希望开发新方法的领域。该项目属于EPSRC转变健康和医疗保健的战略优先事项。最近，人们对开发将随机对照试验的证据与观测数据相结合的方法越来越感兴趣。这些方法旨在利用来自观测数据的非常精确但往往有偏见的估计，并将它们与来自随机试验的潜在噪声但内部一致的估计结合起来。在博士学位之前的小型项目过程中，我们提出了一种新的方法，结合了之前提出的两种解决方案的方面。这种方法的目的是首先调整随机数据集中隐藏混杂因素的影响，然后根据调整的效果合并调整后的数据。我们在早期的模拟中取得了一些令人鼓舞的结果，但我们的目标是进一步改进这种方法，并在更广泛的环境中测试它，包括使用半合成和真实世界的数据。也有可能以新的方式结合其他方法来改进推理。除了开发进一步的方法来改进推理之外，我们的第二个目标将是改进该领域目前的理论基础，目前的方法往往依赖于大量的假设。这个方向的一个起点将是调查是否有可能通过在没有进一步假设的情况下将任意偏向的观测数据集添加到随机化数据集来保证CATE估计的改进。或者，能否证明这是不可能的？最后，除了改进使用现有数据的推理外，我们还想探索是否有可能使用贝叶斯实验设计更有效地收集数据，以最大化我们预期的信息收益。在这一领域有一系列现有的研究，尽管这些方法的应用有点落后于统计文献。首先，我们想探索是否有可能设计更有效的随机对照试验，在较少参与者的情况下实现与CATE估计类似的精度。最终，我们希望我们能够使用这些方法来改进因果发现的实验设计。医学研究的快速增长既带来了机遇，也带来了挑战。为了充分利用多样化的数据源，需要先进的统计方法。我们的目标是改进数据组合技术，了解理论限制，并使用贝叶斯实验设计，这将有助于得出更可靠的结论并推动该领域的发展，支持ESPRC的战略医疗优先事项。