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Bayesian dose adaptive trials using non-myopic response-adaptive methods

  • Full or part time
    Dr S Villar
    Dr D Roberston
  • Application Deadline
    Tuesday, January 07, 2020
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

In dose-finding studies, the aim is to find the maximum tolerated dose of an agent or to find a dose which is closest to a target. In dose-ranging studies, different doses of an agent are tested to establish which dose works best and/or is least harmful by estimating a response-dose relationship. However, achieving either of these goals with a high precision can imply exposing a large number of patients to highly toxic doses, imposing a learning-earning trade-off. Despite extensive recent work in using decision theory for addressing such a trade-off in the context of designing clinical trials [1], little work has been done to extend such a framework for dose-finding/dose-ranging studies. Using a decision-theoretic approach takes into account the interests of the patients both within and outside the trial to derive a patient allocation rule which can acknowledge the existing conflict between the interests of each individual patient and the following patients. This idea was proposed earlier in the literature (e.g. a framework for dose-finding trials using the theory of bandit problems was proposed by Leung and Wang [2]) yet because finding the optimal strategy for this type of bandits with dependent arms is in most relevant cases not computationally feasible, the approach has not been further developed.

This PhD project will look at developing decision-theoretic non-myopic response-adaptive dose-ranging methodology for dose-ranging and dose-finding studies. The project will make use of recent advances in bandit theory to try to reduce the computational complexity of finding the optimal (or nearly optimal) solution derived from a set of relevant optimisation problems. The PhD will cover some of the following areas:
• Use and extend existing response-adaptive randomisation rules to be incorporated into the design of dose-escalation studies.
• Investigate novel optimal response-adaptive adaptive designs that can handle multivariate conflictive outcomes (efficacy-toxicity).
• Asses how these methods perform in terms of estimation purposes and patient gain decisions (administering doses nearest to the target toxicity level).
• Use of the dynamic optimisation (bandit) literature to develop suitable and practical non-myopic adaptive randomisation methods specifically designed for dose adaptive trials;
• Produce easy to use software in R and/or Stata to implement methods;
• Compare the resulting decision-based designs to the real trial.

Funding Notes

The MRC Biostatistics Unit offers 4 fulltime PhDs funded by the Medical Research Council for commencement in April 2020 (UK applicants only) or October 2020 (all applicants). Academic and Residence eligibility criteria apply.

In order to be formally considered all applicants must complete a University of Cambridge application form. Informal enquiries are welcome to

Applications received via the University application system will all be considered as a gathered field after the closing date 7th January 2020

For all queries see our website for details View Website

References

1. Villar, S., Wason, J. and Bowden, J. (2015) Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges. Statistical Science Vol. 30, No. 2, 199-215.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4856206/

2. Leung, D. and Wang, Y.G. (2002) An extension of the continual reassessment method using decision theory Statistics in Medicine 21(1):51-63
https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.970

3. Fan, S. and Wang, Y.G (2006) Decision-theoretic designs for dose-finding clinical trials with multiple outcomes. Statistics in Medicine Vol. 25 No 10 1699—1744
https://doi.org/10.1002/sim.2322

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