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

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.