Design and Analysis of Basket and Umbrella Trials

Prior to treatments' release onto the market, they undergo rigorous testing in clinical trials. Patients responses to such treatments can vary based on their intrinsic factors, and thus it is desirable to target treatments to patient's presenting different characteristics. To address this issue, so-called basket and umbrella trials are proposed. Basket trials consist of a single treatment applied to different patient groups who share a common characteristic but suffer from different diseases. Umbrella trials are composed of multiple treatments tested in parallel on patients who share the same disease but present a different genetic make-up.

We must also consider that, although the primary goal of a trial is to identify the most effective treatment, a secondary goal is to deliver the best treatment to patients within the trial. This is achieved through response-adaptive randomisation, a technique that alters the chance of being allocated a treatment on the basis of data from previously treated patients within the trial.

STOR-i and Roche have collaborated to create a project that aims to answer the following questions: How do we borrow information between treatment groups in basket trials? How can we utilize response-adaptive randomisation to improve the number of patients benefiting from the trial? And finally, how can we change the study populations of sub-studies within umbrella trials in order to incorporate newly identified targeted treatments? To tackle these problems, we first focus in on borrowing information between sub-studies and how to efficiently add a sub-group to a basket trial within this borrowing structure. Adding a sub-group is a highly beneficial feature of the aforementioned trial designs, as a new patient population may be identified part way through the trial. Following this, we will explore the operating characteristics of response-adaptive randomisation and how it is impacted on by temporal changes in the disease or health care provision.

In partnership with Roche.

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.