Double machine learning methods for estimating causal treatment effects on cancer survival outcomes in the presence of high-dimensional confounding

双机器学习方法，用于估计高维混杂情况下因果治疗对癌症生存结果的影响

• 批准号: 2580713
• 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: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2580713
• 关键词: Double; machine; learning; methods; estimating

The treatment of non-small cell lung cancer (NSCLC) is a complex process, and finding a treatment that works often requiring iterative combinations of immunotherapy, surgery and chemotherapy. Ideally, we would know the best course of treatment for each patient using evidence from clinical trials, but often this only includes one treatment at a time, and not complex combinations or long follow-up times. Using routinely collected electronic health records (EHR) and genomic data can help filling the gaps in knowledge. A key step towards finding the best treatment is estimating the treatment's causal effect on survival outcomes and whether this depends on patients' characteristics. Knowing this can help us to predict the (hypothetical) outcomes that can be expected under specific treatment for a new patient, thus eventually allowing clinicians to personalise treatments. Because in routine practice, treatment is given to patients that need it based on their characteristics, observational data is subject to complex, high-dimensional confounding. Recent developments in causal inference allow us to incorporate machine learning for modelling of high-dimensional complex relationships. However, to truly understand which treatment regime is most beneficial for an individual, we need to be able to study the effects of treatment combinations on a patient's survival over time. This project will focus on developing causal machine learning methods for survival outcomes allowing for the time varying nature of treatment decisions. With large, high dimensional datasets becoming common place in the medical setting, this work will not only enable us to enrich the information known about the treatment of non-small cell cancer, but will open the door for many other disease areas to replicate the research methods; providing healthcare professionals with more accurate information on which treatment regimens are most suitable for each of their patients.

非小细胞肺癌 (NSCLC) 的治疗是一个复杂的过程，寻找有效的治疗方法通常需要免疫疗法、手术和化疗的反复组合。理想情况下，我们会利用临床试验的证据了解每位患者的最佳治疗方案，但这通常一次只包括一种治疗，而不是复杂的组合或长时间的随访。使用定期收集的电子健康记录 (EHR) 和基因组数据有助于填补知识空白。 寻找最佳治疗的关键一步是评估治疗对生存结果的因果影响，以及这是否取决于患者的特征。了解这一点可以帮助我们预测新患者在特定治疗下可以预期的（假设）结果，从而最终允许临床医生进行个性化治疗。 由于在常规实践中，治疗是根据患者的特征来给予有需要的患者，因此观察数据容易受到复杂、高维度的混杂因素的影响。因果推理的最新发展使我们能够结合机器学习来建模高维复杂关系。然而，为了真正了解哪种治疗方案对个人最有益，我们需要能够研究治疗组合随着时间的推移对患者生存的影响。该项目将专注于开发因果机器学习方法来实现生存结果，从而考虑到治疗决策的时间变化性质。 随着大型、高维数据集在医疗环境中变得普遍，这项工作不仅使我们能够丰富有关非小细胞癌治疗的已知信息，而且将为许多其他疾病领域复制研究方法打开大门；为医疗保健专业人员提供更准确的信息，了解最适合每位患者的治疗方案。