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

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