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Adaptive Designs for personalised treatment strategies in early phase clinical trials


   MRC Biostatistics Unit

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  Dr Pavel Mozgunov  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

The common goal of phase I in-patient clinical trials is to determine the maximum tolerated dose (MTD), the highest dose level with an acceptable level of toxicity. A number of statistical methods have been proposed to increase the efficient of identifying the MTD. However, all of them treat patients as identical, and therefore aim at finding a single MTD for all patients. At the same time, the patients can react differently to the treatment. For example, the degree of treatment history is known to be associated with people’s tolerance to new treatments [1]. At the same time, trials using more individualised approaches have been found to have higher overall success probabilities [2], and trials using more informative endpoints were found to require fewer patients to achieve accurate conclusions [3].

This project will focus on developing novel efficient adaptive designs for early-phase clinical trials that will allow for personalisation of the treatment (e.g. dose). Firstly, the project will consider the question of including patients’ characteristics (e.g. biomarkers) and the uncertainty around it into the model. Incorporating this information is just starting to establish in early phases due to a limited knowledge on which covariates influence patients' responses meaningfully. One of the challenges here is a limited sample size typical for Phase I trials. To increase the efficiency, secondly, the approaches that use richer (than typically dichotomise) endpoint will be developed. This will include evaluating toxicity and efficacy simultaneously, considering ordinal and continuous outcomes and endpoints measured over several treatment cycles [4] that were found to be of importance for novel therapies, e.g. immunotherapies. Finally, the project will focus on developing approaches allows for intra-patient escalation. The majority of designs for first-in-patient studies assume that patients can receive one dose only [5]. While gradually increasing a dose given patient’s responses is common in late phases, this is rarely formally employed by designs in first-in-patients Phase I trials.

To enhance the real world utility the project will benefit from the collaboration with Prof Xavier Paoletti (Institute Curie, Paris, France), a leading statistician in a number of Phase I cancer trials and a world-leading expert in advanced dose-escalation designs; and Dr Alun Bedding (Roche, Welwyn Garden City, UK), the head of the methodology group with extensive experience in the design of innovative trials who will advise on the application of the developed methodologies in the industry-conducted clinical trials.

Candidates should have a good fundamental knowledge in statistics, should be able to solve complex mathematical problems, understand the assumptions/limitations of common statistical methodologies, and to be able to see the application of theoretical methods in medical research. Candidates should have first degree BSs in Statistics and MSc in Statistics, should have good R programming skills, and should have demonstrated an interest in the career of the statistical methodologist and in undertaking research on emerging questions in medical research.

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:

http://www.methodologyhubs.mrc.ac.uk/about/tmrp-doctoral-training-partnership/

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.

References

1] Rosenblum et.al (1999). Phase I study of 90y-labeled b72.3 Am As Cancer Res 5, 953–961.
[2] Wong, Siah, Lo. (2019). Estimation of clinical trial success rates related parameters. Biostatistics, 20(2), 273-286
[3] Mozgunov, Jaki, Paoletti. (2020). Using a dose‐finding benchmark to quantify the loss incurred by dichotomization in dose‐ranging. Biometrical Journal, 62(7), pp.1717-1729.
[4] Altzerinakou, Collette, Paoletti (2019). Cumulative Toxicity in Targeted Therapies: What to Expect at the Phase II Dose. JNCI.
[5] Aiono’s, O'Quigley (2014). Adaptive dose-finding studies. J. C. Onc.32(23), 2505
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