In clinical trials, a surrogate outcome is one which is used in place of the true clinical outcome of interest. Surrogates are often measured earlier in the follow up period, such that making a final conclusion from the trial based on surrogate outcome findings could potentially accelerate the drug development process, leading to licensed treatments becoming available to patients sooner and reducing the costs associated with developing new treatments.
To gain these benefits, we need to be assured that any decision based on a surrogate outcome would agree with the trial findings had the true clinical outcome of interest been recorded. Statistical methodology for surrogate outcome evaluation has developed in recent decades, stemming from the ground-breaking work of Prentice (1989) and being further developed through causal inference (Frangakis & Rubin, 2004) and the multi-trial and information theory frameworks (Alonso & Molenberghs, 2007).
The validity of surrogate outcomes currently in use or which are accepted as part of drug licensing applications by regulatory authorities has not recently been reviewed. Furthermore, although quantities such as the “surrogate threshold effect” have been proposed, consensus is currently lacking on which metrics should be used to determine whether a surrogate outcome has acceptable performance for it to be useful in practice. This PhD will address both of these issues.
This PhD will systematically review the current status of validated surrogate outcomes, cross-referencing the findings with those outcomes accepted by regulatory authorities in drug licensing applications. This work will identify surrogate outcomes currently in use which have been formally validated, will determine the methods used to evaluate the surrogate and the criteria by which its performance was judged. Surrogates currently in use which have not yet been formally validated will also be identified. Through analysis of individual patient data from completed clinical trials, and simulation studies linked to those data, recommendations will be developed for practical methods of surrogate outcome evaluation, using the framework of Lassere (2008) as a starting point. Potential topics for more detailed study include (a) the role of surrogate outcomes in the development of core outcome sets; (b) the relevance of surrogate outcomes in early phase trials; (c) the evaluation of surrogate outcomes in long-term trials such as dementia prevention studies; (d) the use of decision analysis to guide the optimal evaluation criteria for surrogates. The overall findings will guide drug development strategy, identifying areas in which the use of surrogate outcomes would improve efficiency in the development pathway.
The student will have the opportunity to gain training at University of Edinburgh and other MRC-NIHR Trials Methodology Research Partnership (TMRP) institutions, and to attend meetings organized for the wider cohort of TMRP PhD students.
• Prof Christopher Weir, Edinburgh Clinical Trials Unit, Centre for Population Health Sciences, The University of Edinburgh.
• Prof Steff Lewis, Edinburgh Clinical Trials Unit, Centre for Population Health Sciences, The University of Edinburgh.
• Dr Hannah Ensor, Edinburgh Clinical Trials Unit, Centre for Population Health Sciences, The University of Edinburgh.
In collaboration with:
• Prof Paula Williamson, Institute of Translational Medicine, Faculty of Health and Life Science, University of Liverpool
A strong academic track record with a 2:1 or higher in a relevant undergraduate degree, or its equivalent if outside the UK. It is also desirable to have a strong performance in a relevant postgraduate degree. Proven experience in systematic reviewing and/or statistical modelling is desirable. The successful candidate will work in a highly interdisciplinary environment and should be able to work independently and in collaboration with members of the MRC-NIHR Trials Methodology Research Partnership.
Following interview, the selected candidate will need to apply and be accepted for a place on the Usher Institute Population Health Sciences PhD programme. Details about the PhD programme can be found here: http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=213
Please provide a CV, a personal statement detailing your research interests and reasons for applying, degree certificate(s), marks for your degree(s) and 2 written academic references. All documents should be in electronic format and sent via e-mail to: [email protected]
The closing date for applications is: 5 April 2019. Interviews will be held during April 2019.