Developing efficient perpetual platform trials to study multiple treatments and multiple biomarkers

Clinical trials are used to test the effectiveness and safety of new treatments. In recent years they have become more and more expensive and have high rates of failure. New technology means that a large amount of biological information (summarised by measurements called biomarkers) can be measured. Often this information is likely to tell us how much a patient will benefit from being given a treatment. To make best use of this information, new approaches to clinical trials are needed.
A new type of clinical trial design allows testing of the effectiveness of several new treatments simultaneously while considering that the effect may depend on biomarker measurements. A number of real trials are using this approach. Some of these trials will allow new treatments to be included as they become available. This has some benefits and makes the cost of developing and testing treatments lower. However the effect of doing this on important characteristics of the trial, such as the chance of incorrectly recommending a poor treatment for use in practice, is not well understood.
We will work on ways of doing these trials in the most appropriate way. This will include getting a good understanding of how the trials work. It will also involve developing the best approaches to when new treatments should be added and when treatments in the trial should be recognised as being effective.
Our team includes a wide range of expertise that will mean we can develop suitable methods and support their use in real trials. The developed methods will help patients on trials have a better chance of getting the most suitable new treatment. It will also mean better quality information comes from the trial and that future patients will be treated more effectively.

The availability of affordable biotechnology has meant that many biomarkers can be measured to predict the effect of different treatments. Novel trial designs have been proposed to test the effects of multiple treatments in multiple biomarker subgroups. Several of these are currently being implemented in practice. Many of these trials will allow new treatments and biomarkers to be added as the trial continues for logistical and administrative reasons. We refer to such trials, in which treatments are continually added and removed, as 'perpetual'. Statistical methodology has been lacking for such trials and in particular for when treatments are testing in distinct biomarker subgroups.
We aim to develop methodology for improving several aspects of perpetual trials with multiple treatments and biomarkers. This includes: 1) extending current to allow optimal planning of enriched phase III studies from the results of a perpetual phase II study; 2) developing optimal decision rules for when to drop a treatment from a perpetual trial; 3) understanding the effect of adding in a new arm to a perpetual trial; 4) determining when a new arm should be introduced based on factors internal and external to the trial.
We will use motivating examples of trials that our group has links with in order to inform simulation studies. These trials represent a range of trial designs. In addition to motivating the work, links with these trials provides additional routes for dissemination of the methodology.

In addition to academic beneficiaries, described in the 'Academic beneficiaries' section, this work will benefit a number of other stakeholder groups.
The first stakeholder group is researchers working in clinical trials. The work in this grant will lead to useful methodology and open-source software which will mean better trials can be conducted and more information is available on the benefits and drawbacks of conducting perpetual biomarkers.
The second group is clinicians, both those working in clinical trials and those who are not. Clinicians who are running clinical trials will have the benefits described above: new designs which are more ethical and will provide better information about which subgroups of patients benefit from the treatment. New methodologies develop will mean clinical trials that provide more information on the effect of treatments in subgroups will be run. In the longer term (within 10 years), this may lead to improved quality of information for a clinician deciding how to treat a patient in the clinic.
A third stakeholder is the pharmaceutical industry, which is increasingly interested in funding and contributing drugs to large scale platform trials. This research will be to their benefit as it will help ensure robust evidence is provided from this investment.
A fourth stakeholder includes funding bodies and. This research will provide useful evidence on the effect of adding in arms which will allow these groups to decide whether a proposed trial design is appropriate.
Most of these outcomes will be realised within the length of the grant. Because we are focusing on novel and efficient approaches, longer term outcomes could be contributing towards making the UK become a more attractive place to run these types of trials, meaning more investment from pharmaceutical companies and better quality research.