R2 Bayesian dose adaptive trials with multiple outcomes

The MRC Hubs for Trials Methodology Research (HTMR) Network has one non-clinical studentship available for early 2018 entry. Below is a description one project which can be applied for.

Location: University of Cambridge Hub: MRC Biostatistics Unit

Please contact the named supervisor in advance of submitting your application.
Please see our website for further details, guidance and application form.

https://www.methodologyhubs.mrc.ac.uk/about/phd-studentships/

Background to the project
A recently completed dose-ranging trial (Todd et al., PLOS Medicine, 2016) was designed to find two targeted doses of the biological agent Proleukin that resulted in a 10% or 20% immune response (as measured by the change in the amount of regulatory T-cells) in newly diagnosed diabetes patients. Proleukin is administered by injection and any dose can be administered within the safe therapeutic range. Dose decisions were made by using optimal design theory of minimising the variance of the doses that gave the targeted responses. A follow-up study is planned to identify the best dose and frequency of repeat administration of dose. The primary outcomes are laboratory measurements of three blood-based markers and dose-changing decisions are made using a multivariate regression model.

What the studentship will encompass
This PhD project will look at extending existing dose-ranging methodology by designing and investigating novel optimal adaptive designs that can handle multivariate outcomes. These new approaches will be closer to the real world situations faced in the decision-making process early on in dose-ranging clinical trials. This may include aims such as:
1) Quantify the information loss by using dimension reduction techniques, such as principle components analysis and using univariate outcomes;
2) Investigate model-robust methods such as Bayesian model averaging techniques and likelihood-based information methods (previous BSU PhD work);
3) Using historical data to inform dose-changing decisions, e.g. using data from the single Proleukin dose study in the second study via techniques such as commensurate and power priors (Hobbs et al., Biometrics 2011);
4) Exploring fully Bayesian approaches to handle uncertainty in parameter estimates;
5) Using penalised D-optimality methods (Pronzato, J. Stat. Planning 2010) when safety endpoints are in the multivariate outcome;
6) Produce easy to use software in R and/or Stata to implement methods.

Detail of supervision
The main supervisor will be Dr Adrian Mander, who will meet with the student weekly.

Secondments/industry placement
There are no planned industry placements, but the methods developed will be very relevant to the pharmaceutical industry. Dr Mander has collaborations with GSK, Roche and AZ in the area of adaptive dose designs.

General enquires [Email Address Removed]
Supervisor Dr Adrian Mander, [Email Address Removed]

Deadline: 8 January 2018 at 4pm (GMT)