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Enhancing the design, conduct and analysis of randomised adaptive trials through consensus-driven Statistical Analysis Plan guidance

   Clinical Trials and Statistics Unit

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  Prof Christina Yap, Dr Munya Dimairo, Assoc Prof F König, Dr S Villar  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project


We need efficient, innovative and robust trial designs to swiftly test new treatments, and ensure safe and effective ones reach patients quickly. Adaptive designs are such innovative approaches that have proved their efficiency and are being used more often. Adaptive designs allow pre-specified changes (adaptations) to trial aspects to be made by analysing interim data while making valid conclusions. 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.1

Greater flexibility and efficiency come at the cost of additional operational and statistical complexities. Using interim analyses to make adaptations complicates the design, conduct, analyses, and reporting. Adaptations should be accounted for in the design and all analyses; otherwise, results can be misleading. Existing SAP guidance does not comprehensively cover considerations for adaptive designs.2,3 Insufficiently detailed statistical methods of adaptive designs in SAPs will make it difficult to reproduce methods, interpret results, and create mistrust about the trustworthiness of results - undermining public confidence in research leading to research waste.

What the studentship will encompass:

The overarching aim is to enhance the SAPs of adaptive designs in clinical trials, and will comprise of the following main components:

(a)  Review existing literature of current practice to assess quality of reporting and identify gaps and aspects that should be included in the SAP;

(b)  Conduct a qualitative study on trialists’ experience in the writing of SAPs with adaptive designs and perceptions about potential areas of improvement;

(c)   Use a modified Delphi process to develop stakeholder-engaged (including statisticians, clinicians, trial managers, patients/carers, regulators and funders) evidence-based consensus recommendation in SAP for adaptive designs.

(d)  Develop the SAP guidance and provide practical useful examples using ongoing and completed trials with adaptive designs to maximise uptake of the finalised guidance.

Expected outputs:

At least 3 peer reviewed publications are anticipated in methodological/clinical trial journals.

The supervisory team includes expert methodologists and clinical trialists in adaptive designs. It consists of Professor Christina Yap (Institute of Cancer Research, UK), Dr Munya Dimairo (University of Sheffield, UK), Dr Franz Koenig (Medical University of Vienna, Austria) and Dr Sofia Villar (University of Cambridge, UK).

Secondments of research visits to Medical University of Vienna (Austria), University of Sheffield and University of Cambridge are planned as part of the studentship. This will provide the student the opportunity to learn about the diverse type of adaptive trials carried out in the different institutions and issues relating to their analyses. 

The project will include working with patient and public partners in the wider advisory group to enhance public understanding of adaptive designs and the importance of a high-quality SAP by developing simple ways to explain complex methods to lay audience.

Candidates must already have been awarded a first-class or second upper class honours degree in Mathematics or Statistics. A Master’s degree or equivalent in medical statistics or a related quantitative discipline, or experience of working in clinical trials is desirable.  

Depending on the qualifications of the successful candidate, training in adaptive designs, Bayesian methodology and qualitative analysis will be provided. The training will be supplemented with hands-on experience of writing SAPs for ongoing trials. In addition to the supervisory team, the candidate will also work with a larger multidisciplinary advisory group of expert methodologists and clinicians from ICR, Cardiff University, Universities of Cambridge, Edinburgh and Reading and patient partners to develop the consensus-driven SAP guidance.


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 primary supervisor to find out more about the project and to discuss their interests in the research.

The deadline for applications is 4pm (GMT) 18 February 2022. Late applications will not be considered.

Completed application forms must be returned to: [Email Address Removed]

Informal enquiries may be made to [Email Address Removed]

Funding Notes

Studentships are funded by the Medical Research Council (MRC) for 3 years. Funding will cover tuition fees at the UK rate only, a Research Training and Support Grant (RTSG) and stipend (stipend to include London Weighting where appropriate). We aim to support the most outstanding applicants from outside the UK and are able to offer a limited number of bursaries that will enable full studentships to be awarded to international applicants. These full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme.


1 Pallmann et al.
2 Gamble et al.;
3 DeMets et al.
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