Pavel Mozgunov – email@example.com
Potential PhD Projects
- Seamless phase I/II modular dose-finding designs
It is now common to study combination of treatments to achieve a better efficacy or better tolerability. An emerging setting is to conduct a trial of an experimental drug alone, then in combination, and then to proceed into expansions. Such trials are referred to as modular. A naive (but common) approach is to design each study independently. This can be highly inefficient. The objective is to develop adaptive designs for early-phase modular trials that allow borrowing of information across modules. Basket and platform design ideas will be explored to borrow information and to tackle unplanned changes.
- Design and analysis of trial with treatment schedules
In infectious diseases such as Tuberculosis (TB) and Hepatitis B (HBV), the treatment duration with current standard regimes is lengthy which results in a large burden on the patients. Novel treatments or combinations of treatments in these areas offer the opportunity for both higher efficacy and shorter treatment periods. While the standard methods for considering various treatment durations can be applied, there will be suboptimal due to not taking into account the monotonicity assumption – the longer duration will have higher response rate. The objective of the project is to develop novel adaptive designs for trial involving treatment schedules that will exploiting the natures of the various schedules to gain efficiency in the decision-making.
- Response-adaptive design based on the weighted information measures
A class of Bayesian designs based on a novel concept of weighted information measures has been proposed recently. Such designs allow to take into account the desirability of outcomes together with the uncertainty around them (while standard information measures account for the latter one only). This results in a more ethically viable approach assigning more patients to better performing arms while not compromising the integrity of a trial. This class of designs was originally developed for multinomial endpoint. The objective of the project is to work on the generalisation of the information-theoretic concept to continuous outcomes using various type of information (Shannon, Fisher, Tsallis), its estimation, and on a randomised setting with the weighted information measure accounting for comparisons to the common control.
How to apply
For details of the MRC BSU application process please see How to apply
To be considered for funding applications need to be submitted to the University of Cambridge application system by 23:59 (GMT) on January 5th 2023