Design and Analysis of Platform Trials.

Bringing a new treatment to market is a long and expensive process, which can often end in failure. Platform trials are a class of clinical trials, which aim to increase efficacy compared to traditional trial designs via a possibility of adding new treatments to ongoing trials. Therefore, a statistical methodology for platform trials that allows new experimental treatments to be tested as efficiently as possible while satisfying the regulatory bodies' standards is essential. Therefore, STOR-i and Roche have partnered together in order to create a project which is focused on answering the following three questions:
1. When, why and how to add new treatments to an ongoing study?
2. How can a sequence of trials be designed?
3. How can a trial be best designed which has analyses conducted part way through the trial and where the trial focus is on comparing each treatment to one another?
The initial aim of the project is to develop methods that allow for the addition of new experimental treatments as the trial progresses. This is beneficial as during a course of confirmatory clinical trials - which can take years to run and require considerable resources - evidence for a new promising treatment may emerge. Therefore, it may be advantageous to include this treatment into the ongoing trial as this could benefit patients, funders and regulatory bodies by shortening the time taken comparing and selecting experimental treatments, thus allowing optimal therapies to be determined faster and reduce costs and patient numbers. The key part of the solution for this problem is making sure that the correct number of patients are recruited and that only treatments with enough evidence that they are better than the control treatment go on to the next phase, in order to meet regulatory bodies' standards. After studying this question, the methodology will then be further developed for the other two questions.

This proposal will benefit (i) the UK economy and society, (ii) our industrial partners, (iii) the wider community of non-academic employers of doctoral graduates in STOR, (iv) the scientific disciplines of statistics and operational research and associated academic communities, (v) UK doctoral students in STOR, and (vi) the CDT students themselves.

Below we outline how each of these communities will realise these benefits:

(i) The UK economy will gain a competitive edge through a significant increase in the supply and diversity of doctoral STOR professionals with the skills required to undertake influential, responsible and impactful research, and who have been trained to become future leaders. Our goal is that our future alumni who enter industry assume leading roles in realising the major impact that STOR can make in achieving effective data driven decision-making. Our existing alumni are already starting to achieve this. A wider societal benefit will accrue from research contributions to EPSRC Prosperity Outcomes, e.g. to the UK being a Productive and Resilient Nation.

(ii) Our industrial partners will particularly benefit from the skills supply identified in item (i), as likely employers of STOR-i graduates. They will further benefit from teaming with a community of leading edge STOR researchers in the solution of substantive industrial challenges. Mechanisms for the latter include doctoral projects co-supervised with industry, industrial internships, engagement in research clusters and industrial problem-solving days. Our training programme will give students the skills they need to ensure that research is conducted responsibly and that outcomes are successfully communicated to beneficiaries. The value that our industrial partners place on working with STOR-i can be seen through the pledged cash support of £1.7M.

(iii) A wider benefit will accrue from the employment of STOR-i graduates, equipped as described in items (i) and (ii), across non-partner public and private sector organisations. The breadth and depth of training provided by the CDT will enable students to quickly make a difference in these organisations, using their research skills to affect significant change.

(iv) The STOR academic community will benefit from methodological advances and from the increase and diversity in the supply of STOR researchers who value, and have experience of, collaborative research. Our alumni will be leaders in 21st Century Statistics with a strong culture of, and training in, reproducible research and a focus on achieving impact with excellence. Our recruitment strategy will further benefit this community in achieving a healthier supply of high-quality doctoral candidates from diverse backgrounds. Our research internship programme gives top mathematically able individuals from across the UK an experience of STOR research and has been shown to increase applications for STOR PhD programmes across the UK.

(v) Elements of the STOR-i programme will benefit the wider community of UK doctoral students in STOR. Using financial support from our industrial partners, we will continue our National Associate Scheme. This will provide up to 50 UK STOR doctoral students with funding and access to elements of STOR-i's training programme. An annual conference will provide opportunities for learning, networking and sharing research progress to members of the scheme.

(vi) STOR-i students will benefit from a personalised programme that will support each individual in fully achieving their research leadership potential, whether in academia or industry. Students will be given the tools and opportunities to develop research and broader skills that will enable them to achieve maximum scientific impact for their work. Our current alumni provide strong evidence that these future graduates will be extremely employable.