Lung cancer is the leading cause of cancer mortality in western society. Small cell lung cancer (SCLC) is particularly aggressive with 5-year survival of only 10%. Standard-of-care treatments are radiotherapy, chemotherapy, and more recently immunotherapy. However, oncologists often need to adjust standard regimens, for example by reducing doses, to meet the individual needs of their patients, and there is still significant uncertainty around how best to treat patients, such as the elderly, frail, those with other health needs, and the socioeconomically deprived, who are not well represented in clinical trials.
Real-world data, the information recorded about patients’ treatment and outcome as part of their normal care, has the potential to provide evidence where clinical trial data does not or will not exist and support decision-making in such patient groups. Conventional risk modelling analyses the associations between patient outcomes and clinical factors without inferring any causal relationship. As such these models can tell us if a patient is at high risk of an adverse outcome and something needs to be done, but cannot tell us what we should do to improve that patient’s outcome (i.e. how their treatment should be changed). The inability of conventional risk stratification to inform how many groups of SCLC patients should be treated, combined with the lack of clinical trial data in some patient groups, is a serious unmet clinical need.
In this project, we will develop and apply the newly emerging techniques of causal inference to enrich prediction models to directly predict the effect of hypothetical interventions/decisions from observational real-world datasets. We will develop a new class of causal predictive models for SCLC patients, and explore pragmatic routes to clinical impact by targeting their use where they can have the most benefit, such as in helping broaden the experience of trainee oncologists.
Entry Requirements
Candidates must hold, or be about to obtain, a minimum upper second class (or equivalent) undergraduate degree in a relevant subject. A related master’s degree would be an advantage.
Applicants interested in this project should make direct contact with the Primary Supervisor to arrange to discuss the project further as soon as possible.
How to Apply
To be considered for this project you MUST submit a formal online application form - full details on how to apply can be found on the CRUK Manchester Centre PhD Training Scheme (MCRC) website https://www.bmh.manchester.ac.uk/study/research/funded-programmes/mcrc-training-scheme/
General enquiries can be directed to [Email Address Removed].
Equality, diversity and inclusion is fundamental to the success of The University of Manchester and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website https://www.bmh.manchester.ac.uk/study/research/apply/equality-diversity-inclusion/
The CRUK RadNet Manchester Unit was one of only three major units awarded. It builds on the 10-year history of an external collaborating “One Manchester” approach to cancer team science in radiotherapy-related research (RRR). This has been achieved by our multi-disciplinary expertise in biology, clinical oncology, physics, software development, engineering and imaging. Manchester is recognised nationally as a Clinical and Translational Radiotherapy Research Working Group (CTRad) Centre of Excellence in Radiotherapy Research and the only centre in the UK with strength across all disciplines (biology, clinical, physics, technology).
The CRUK RadNet Manchester Unit Vision statement is: “As an integrated world-leading translational radiation oncology programme, we address the challenges of diverse patient characteristics to achieve individualised physical and biological targeting based on real-time outcomes and a deep mechanistic understanding of immune response, comorbidity and genomics.” This Vision aligns with CRUK’s research strategy through Collaborative Hubs and new science.
Interview date – WB 4 April 2022
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