We need efficient, innovative and robust early phase I/II trial designs to swiftly test new cancer therapies and ensure safe and effective ones are being progressed to later stage trials. Recent advances in precision oncology (which aims to match cancer treatments to the patient’s unique form of cancer) have motivated innovative trial designs, particularly the idea of master protocol (e.g., basket, umbrella and platform trial), for the evaluation of molecularly targeted cancer therapies. Early phase adaptive platform trials (EP-APTs) are such innovative approaches and indisputably can provide efficiency improvements, statistically or operationally or both: (a) statistically - allowing within-trial information sharing of safety and preliminary efficacy data (and hence increase in precision or power); (b) operationally - evaluating several targeted therapies for one (or more) diseases concurrently, and accepting additions of new treatment arms or patient population during the trial. EP-APTs often also incorporate pre-specified changes (adaptations) to trial aspects to be made by analysing interim data. Such adaptations include early stopping of treatment arm(s) as soon as enough evidence is gathered or allocating more patients to treatments showing greater benefits.[2,3]
The student will be based within ICR-Clinical Trials and Statistics Unit (ICR CTSU) and work closely with the Drug Development Unit where they will benefit from real-life experience working with specialists on early phase cancer clinical trials. The objectives of this project are to:
(a) Review existing literature of the design and analysis approaches of early phase platform trials in precision medicine.
(b) Develop new approaches motivated by ongoing early phase adaptive platform trials conducted at ICR to assess interesting methodological issues, such as the use of reliable short-term outcomes, joint evaluation of safety and efficacy, decision criteria, response-adaptive randomisation and pooling of information across arms using Bayesian techniques.
(c) Test statistical properties of selected trial designs and analysis strategies
(d) Incorporate newly developed efficient methodologies into ongoing clinical trials or influence the methodologies of future such trials.
At least 3 peer reviewed publications are anticipated in methodological/clinical trial journals.
The supervisory team includes early phase expert methodologists Christina Yap, Team Leader of Early Phase and Adaptive Trials at the ICR and Professor Ken Cheung, Columbia University; and clinicians: Dr Juanita Lopez and Professor de Bono at the Drug Development Unit at Royal Marsden Hospital and ICR.
A research visit to Columbia University (US) to work with Prof Ken Cheung and his methodology group can be planned as part of the studentship. Such visit will provide the student an invaluable opportunity to work with international leading adaptive design specialists.
The project will include consulting patient and public partners on their views of the novel features of such adaptive platform trials and the additional efficiencies they offer. The student will also be encouraged to work with patient and pubic partners to co-develop simple ways to explain complex methods to lay audience.
HOW TO APPLY
You are applying for a PhD studentship from the MRC TMRP DTP. A list of potential projects and the application form is available online at:
Please complete the form fully. Incomplete forms will not be considered. CVs will not be accepted for this scheme.
Please apply giving details for your first choice project. You can provide details of up to two other TMRP DTP projects you may be interested in at section B of the application form.
Before making an application, applicants should contact the project supervisor to find out more about the project and to discuss their interests in the research before 09 January 2023.
The deadline for applications is 4pm (GMT) 16 January 2023. Late applications will not be considered.
Completed application forms must be returned to: [Email Address Removed]
Informal enquiries may be made to Prof Christina Yap - [Email Address Removed]