Estimating treatment effects using real world data when there are competing risks

当存在竞争风险时使用真实世界数据估计治疗效果

• 批准号: 2585149
• 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-2585149
• 关键词: Estimating; treatment; effects; using; real

Randomized controlled trials (RCTs) are the gold standard for establishing evidence for the effects of medical treatments and other interventions. However, the value of evidence on treatment effects from real world data (RWD), such as electronic health records, is increasingly being recognized as it presents opportunities to study treatment effects in large and diverse patient populations. This project will focus on estimating effects of treatments on time-to-event outcomes, such as death due to cancer, using RWD. When estimating treatment effects on time-to-event outcomes it is important to consider competing risks, meaning other events that individuals are subject to. Important examples of competing risks are studies of treatments for prostate cancer where many patients will die not from their cancer but due to other causes, and studies of the impact of statin use on cardiovascular mortality in dementia patients, who also have a high risk of death from other causes. Quantifying treatment effects in this setting is challenging and needs to account for possible effects of the treatments being investigated on other causes of death. Commonly used treatment effect measures when there are competing risks include cause-specific and subdistribution hazard ratios, but recent statistical literature has shown that these do not have a causal interpretation and has recommended alternative treatment effect measures known as estimands.This recent work has focused on RCTs and extensions are required to use the recommended estimands in the analysis of RWD, in conjunction with specialized methods required to address time-dependent confounding. This PhD project will thus evaluate and develop methods for the appropriate handling of competing risks to estimate causal effects of treatments using RWD. The methods will be illustrated in an example of treatments for prostate cancer using linked, national data from cancer registries, hospital and death records.The anticipated outcomes of this project are statistical methods for estimating impacts of multi-level treatments on survival when there are competing risks using RWD, and substantive findings relating to optimal treatments for prostate cancer. This will contribute important evidence for the place of RWD in the regulatory framework and to inform drug development, particularly to inform design of phase III trials which are critical for healthcare decision-making. Strategies to achieve these impacts will include publication of papers in both statistical/epidemiological and clinical journals. I will also present my work at conferences, and conference attendance and presentation skills have been incorporated into my training plan. Through collaboration with the project co-funders, AstraZeneca, I will have opportunities to present my work and engage with experts who inform new studies and policy recommendations.Throughout my studentship, I have obtained subject-matter training from internal courses at LSHTM: Doctoral Transferable Skills Programme, and from external courses: UCL Extend, Inkpath, RSS, SOAS, Society for Epidemiologic Research and Academy for PhD Training in Statistics. The courses include research ethics, effective literature searching skills, data management, statistical computing, advanced programming, high dimensional statistics, computer intensive statistics, statistical machine learning, cancer survival, competing risks, and using simulation studies to evaluate statistical methods. These courses have equipped me with skills on how to create fast and numerically accurate statistical programs needed for this project. In 2022, I presented our work in the Young Statisticians Meeting where we were voted as the best talk and sponsored to present our work at the 2022 RSS conference. In 2023, I presented our work at SGUL statistical seminar and the ISCB44 conference.Keywords: Competing risks, treatment effects, real world data, estimands, causal effect, time-to-event outcomes

随机对照试验 (RCT) 是建立药物治疗和其他干预措施效果证据的黄金标准。然而，来自现实世界数据（RWD）（例如电子健康记录）的治疗效果证据的价值日益得到认可，因为它提供了研究大量不同患者群体治疗效果的机会。该项目将侧重于使用 RWD 估计治疗对事件发生时间结果（例如癌症死亡）的影响。在评估治疗对事件发生时间结果的影响时，重要的是要考虑竞争风险，即个人遭受的其他事件。竞争风险的重要例子是前列腺癌治疗的研究，其中许多患者不是死于癌症，而是死于其他原因，以及他汀类药物的使用对痴呆症患者心血管死亡率影响的研究，痴呆症患者也有很高的死于其他原因的风险。在这种情况下量化治疗效果具有挑战性，需要考虑正在研究的治疗对其他死因可能产生的影响。当存在竞争风险时，常用的治疗效果测量包括特定原因和次分布风险比，但最近的统计文献表明，这些没有因果解释，并推荐了称为估计值的替代治疗效果测量。这项最近的工作重点是随机对照试验，并且需要扩展以在 RWD 分析中使用推荐的估计值，并结合解决时间依赖性混杂因素所需的专门方法。因此，该博士项目将评估和开发适当处理竞争风险的方法，以估计使用 RWD 治疗的因果效应。这些方法将在使用来自癌症登记、医院和死亡记录的关联国家数据的前列腺癌治疗示例中进行说明。该项目的预期结果是使用 RWD 的统计方法，用于在存在竞争风险时估计多级治疗对生存的影响，以及与前列腺癌最佳治疗相关的实质性发现。这将为 RWD 在监管框架中的地位提供重要证据，并为药物开发提供信息，特别是为对医疗保健决策至关重要的 III 期试验的设计提供信息。实现这些影响的策略将包括在统计/流行病学和临床期刊上发表论文。我也会在会议上展示我的工作，会议出席和演讲技巧已经纳入我的培训计划中。通过与项目共同资助者阿斯利康的合作，我将有机会展示我的工作，并与为新研究和政策建议提供信息的专家进行交流。在我的学生期间，我从LSHTM的内部课程：博士可转移技能计划和外部课程：UCL Extend、Inkpath、RSS、SOAS、流行病学研究协会和统计学博士培训学院获得了主题培训。课程包括研究伦理、有效的文献检索技能、数据管理、统计计算、高级编程、高维统计、计算机密集型统计、统计机器学习、癌症生存、竞争风险以及使用模拟研究评估统计方法。这些课程使我掌握了如何创建该项目所需的快速且数字准确的统计程序的技能。 2022 年，我在青年统计学家会议上展示了我们的工作，我们被评为最佳演讲，并得到赞助在 2022 年 RSS 会议上展示我们的工作。 2023 年，我在 SGUL 统计研讨会和 ISCB44 会议上介绍了我们的工作。关键词：竞争风险、治疗效果、真实世界数据、估计量、因果效应、事件发生时间结果