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Development and Implementation of Efficient Adaptive Designs in Early Phase Oncology Trials for Novel Agents

  • Full or part time
    Prof C Yap
    Prof J Bliss
  • Application Deadline
    Sunday, November 17, 2019
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

About This PhD Project

Project Description

The Institute of Cancer Research, London, is one of the world’s most influential cancer research institutes. We are committed to attracting and developing the best minds in the world to join us in our mission—to make the discoveries that defeat cancer.

Development and Implementation of Efficient Adaptive Designs in Early Phase Oncology Trials for Novel Agents

Project Description

Development and Implementation of Efficient Adaptive Designs in Early Phase Oncology Trials for Targeted Agents Cancer therapeutics is a rapidly changing field. New classes of targeted therapies and immunotherapy have mechanisms of action, toxicities, and therapeutic indices that are different from conventional cytotoxic drugs previously encountered, thus potentially rendering current clinical trial methodologies inappropriate. These new classes of therapies present unique challenges in the accurate assessment of clinical safety and efficacy, including late‐onset toxicity, delayed and duration of clinical activity and dose optimisation.

This overarching aim of this PhD project is to develop novel phase I/II clinical trial designs to accommodate the mechanism of actions of targeted therapies and immunotherapies, including delayed toxicity and efficacy outcomes and non-monotonic dose-efficacy relationship.

This project will comprise of two main components:

(1) Development of efficient trial designs
- Review existing literature of early phase designs for targeted agents and immunotherapies.
- Develop novel Phase I/II adaptive designs for evaluating efficacy and toxicity taking into consideration potential delayed outcomes, extending earlier work such as time-efficient Time-to-event Continual Reassessment Method (Cheung and Chappell, 2000) and joint evaluation of efficacy and toxicity such as EffTox (Thall and Cook, 2004) and seamless Phase I/II approaches (Wages and Tait, 2014)
- The proposed approaches would be motivated by real-world cancer trials, with a focus on drugs discovered by ICR scientists.

(2) Practical Implementation
- Software and visual tools (such as Dose Transition Pathways, Yap et al 2017) will be developed to enable ease of implementation of the proposed methodology by trialists.
- This PhD will also provide an excellent opportunity to work closely with clinical investigators on early phase cancer trials to create trial designs tailored to the specific needs of the trial and implement them in practice.

The project will suit a candidate with strong statistical and computational skills. The candidate will be highly numerate with a keen interest in developing statistical methodology and implementing them in cancer clinical trials.

This PhD studentship will join a national cohort of PhD students from over 20 institutions as part of the MRC-NIHR Trials Methodology Research Partnership (TMRP), which brings together a number of national and international networks, institutions and partners undertaking clinical trials and trials methodology research. The overall aim being to improve patient care by improving the way in which the healthcare evidence base is developed.

Keywords /Subject Areas
Biostatistics
Early Phase Cancer Clinical Trials
Adaptive Trial Designs
Trials Methodology
Delayed Outcomes
Medical Statistics

Funding Notes

Students receive an annual stipend, currently £21,000 per annum, as well as having tuition fees (both UK/EU and overseas) and project costs paid for the four-year duration. We are open to applications from any eligible candidates and are committed to attracting and developing the best minds in the world.
See icr.ac.uk/phds to apply
Applications close 11:55pm UK time on Sunday 17th November 2019

Candidates must have a first class or upper second class honours BSc Honours/MSc in Mathematics or Statistics. A Master’s degree or equivalent in medical statistics or a related quantitative discipline would be desirable.



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