Our work packages
The RAPID-RT project is made up of seven strands of research called work packages (WPs).
WP1: Legal, ethical, and regulatory issues around rapid learning
WP1 focuses on the legal, ethical, and regulatory issues surrounding the use of rapid learning to change and optimise radiotherapy practice.
The two-day citizens’ jury held in May 2022 explored approaches to consent and patient information. The views of the 23 jurors were incorporated into the design of the RAPID-RT WP3 Data Study.
Over the next few years, the WP1 team will interview patients eligible for the RAPID-RT data study to understand their views on the study’s opt-out consent approach and participant information strategy. Alongside this, clinicians will be interviewed to understand their views and how they may have changed during the lifetime of the study.
Finally, a three-day citizens’ jury will reach a consensus position of when and how it is ethically acceptable to use rapid learning to evaluate changes in radiotherapy practice.
The WP1 team
Sarah Devaney
Professor in Health Law and Regulation (The University of Manchester)
Sarah is an expert in health law and regulation with a particular interest in how the law can facilitate meaningful informed consent to healthcare treatment.
In this programme, Sarah will apply her expertise to a combined healthcare research and treatment context, analysing what mechanisms can be deployed to ensure patients’ entitlements to express their autonomous wishes are protected and respected in this and future clinical research projects involving rapid learning.
Søren Holm
Professor of Bioethics (The University of Manchester)
Søren is an expert in research ethics and has long-standing experience in researching ‘informed consent’ and other types of research consent conceptually and empirically.
He is a member of the HRA National Research Ethics Advisors Panel. He will contribute to the development and evaluation of the consent model for the project using results from interviews and focus groups to determine what regulatory approvals are needed for the clinical demonstration.
Research associates
- Catherine Bowden
WP2: Statistical approach implementation
The aim of WP2 is to implement the statistical approaches needed to assess the clinical impact of using routinely collected radiotherapy data in the rapid-learning context. Specifically, this work package will begin with an independent quality audit of the data required for rapid learning, so that appropriate curation strategies can be put in place. Next, evaluation methods will be implemented for different types of radiotherapy outcome data. Finally, the potential for patient-level decision support models for individualised radiotherapy treatment dose limits will be explored.
The WP2 team
Evan Kontopantelis
Professor of Data Sciences and Health Service Research (The University of Manchester)
Evan has degrees in computational statistics and machine learning and a long track record in research using large-scale NHS databases to investigate quality-of-care.
His research interests include statistical methods in health sciences with a focus on meta-analysis, longitudinal data modelling, observational studies with electronic health records, machine learning and quasi-experimental designs.
He will contribute to the programme’s methodological development.
Tjeerd van Staa
Professor of Health eResearch, Centre for Health Informatics (The University of Manchester)
Tjeerd is a world expert in conducting clinical trials that use routinely collected data (such as electronic health records), particularly with point-of-care randomisation.
He will contribute to the development of suitable mathematical analysis techniques for rapid learning and to the investigation of the acceptability of different methodological designs for testing the effect of interventions in each rapid learning cycle.
Research associates
- Catharine Morgan
WP3: Rapid learning effectiveness in lung cancer radiotherapy
WP3 aims to demonstrate the clinical effectiveness of rapid learning to prospectively optimise radiotherapy treatments for lung cancer.
The clinical change we will evaluate is the limit to the dose of radiotherapy received by the top of the heart that The Christie NHS Foundation Trust is introducing in early 2023.
This study is referred to as the RAPID-RT data study and uses anonymised routine patient data to assess the effect on life expectancy and side-effects of the new heart dose limit.
Research practitioners
- Harry Crawford
WP4: Treatment changes, EQ-5D usage and rapid learning data system in radiotherapy
WP4 involves generating evidence of the economic effect of reducing the radiation dose to the top of the heart in patients with lung cancer receiving radiotherapy, by calculating the healthcare costs and health consequences compared with standard radiotherapy.
It will also generate evidence of the usefulness of using the standard EQ-5D health measurement tool for capturing the impact of radiotherapy treatment on patients, and of the impact of using a rapid learning data system on health system capacity in the routine radiotherapy setting to inform future modifications to treatment plans.
The WP4 team
Katherine Payne
Professor of Health Economics (The University of Manchester)
Katherine will lead the health economic input for this programme, working with Rob Hainsworth and Gabriel Rogers. Collectively, they will use their expertise in decision-analytic modelling and outcome measurement to deliver WP4.
Katherine has worked at The University of Manchester since 1994. She was awarded a personal chair in health economics in August 2010 and currently is the lead for the Manchester Centre for Health Economics.
She was awarded NIHR Senior Investigator award in April 2023 and is a member of the Inclusivity Research Oversight Board for the Manchester Biomedical Research Centre.
Research associates
- Rob Hainsworth
- Gabriel Rogers
WP5 and WP6: Rapid learning in different cancers and radiotherapy treatments
WP5 and WP6 will investigate whether rapid learning using routine data can be applied to different cancers and radiotherapy treatments across the NHS.
Clinicians will be interviewed to understand their perception of the rapid-learning method and their experiences of implementing rapid learning, with the aim of understanding the barriers to implementation. This will feed into WP7.
The WP5 and WP6 team
David French
Professor of Health Psychology (The University of Manchester)
David French is Scientific Advisory Group member and author for the third edition of the MRC Guidance on Development and Evaluation of Complex Interventions.
He has extensive experience of the design and evaluation of complex interventions, and is currently working on implementation and knowledge transfer projects relating to several interventions.
He will lead on optimising implementation of approach, involving refinement of intervention to ensure acceptability, conducting process analyses, and coordinating formal engagement with stakeholders.
Research associates
- Arbaz Kapadi
WP7: Beyond the project
WP7 brings together the findings of WP1-6, and defines the path forward that will be taken after the end of the programme.
As part of WP7, a stakeholder event is scheduled for July 2023 to celebrate progress to date and discuss the later parts of the programme. This event will also define what constitutes success in the eyes of the wider community.
In the second half of the programme, a consensus will be sought on the value of rapid learning in NHS radiotherapy centres through a two-day meeting with key stakeholders.
Contact Us
+44 (0) 161 306 6000
