Health Data Science CDT

医学领域的研究有了长足的发展，涵盖了广泛的主题，并采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银行，提供了数千人的详细观察数据集。然而，尽管有这些广泛的数据集，如何最好地利用这些新资源仍然存在挑战。通常，类似主题的研究将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，而不是更好的推论。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，就会呈现出令人困惑的画面。改进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的：改进结合观察和随机数据的方法，更好地理解这一过程的理论局限性，并使用贝叶斯实验设计的原则来设计更多信息丰富的实验。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改革卫生和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察数据结合起来的方法越来越感兴趣。这些方法旨在利用观察数据中非常精确但往往有偏差的估计，并将它们与随机试验中可能有噪声但内部一致的估计结合起来。在攻读博士学位之前的迷你项目中，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各个方面。该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进行测试，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，我们的第二个目标将是改进该领域当前的理论基础，其中方法通常依赖于实质性的假设。在这个方向上的一个起点将是调查是否有可能通过在随机数据集中添加任意偏差的观测数据集来保证CATE估计的改进，而不需要进一步的假设。或者，可以证明这是不可能的吗？这两项发现中的任何一项都将是对该领域的新贡献。最后，除了使用现有数据改进推理外，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的预期信息增益。在这一领域有一系列现有的研究，尽管这些方法的应用在一定程度上落后于统计文献。最初，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，在CATE估计中达到相似的精度。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。医学研究的快速发展带来了机遇和挑战。为了充分利用各种数据源，需要先进的统计方法。我们的目标是改进数据组合技术，理解理论限制，并使用贝叶斯实验设计，这将有助于得出更有力的结论，推动该领域的发展，支持ESPRC的战略医疗保健优先事项。

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.