Network Meta-Analysis and Extrapolation of Survival Outcome Data

PhD project (3/4 yr research project leading to independent research at the doctorate level)

Nicky Welton, Tony Ades,, Martin Hoyle, Jamie Peters, William Henley (Exeter)

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Health Technology Assessment (HTA) is used to inform policy decisions on whether to recommend treatments on the NHS, on the basis of whether health benefit gains represent good value for money. Impact of treatment on life-expectancy is usually a key driver of cost-effectiveness. Robust estimates of mean life expectancy differences between competing treatments are therefore required. This is challenging because: (i) randomised controlled trials (RCTs) typically follow up patients for only a few years; (ii) there may be multiple treatments and studies, requiring network meta-analysis; (iii) for cancer there are two related outcomes: progression-free and overall survival; (iv) treatment switching may occur after disease progression.

Aims & objectives

Recently methods have been developed to extrapolate RCT evidence from a single trial over a lifetime, by calibrating it to registry data. This project will extend this approach to network meta-analysis of multiple trials for multiple treatments. This will involve flexible survival models (eg spline, fractional polynomial, or mixture models), consideration of joint modelling of progression-free survival and overall survival, and adjustment for treatment switching.


The student will learn complex cutting edge methods for network meta-analysis, analysis of survival data, combining evidence from different sources (RCT evidence and “real world evidence”), and treatment switching. They will develop new methods that combine all of these ideas, and apply them to recent examples in NICE technology appraisals. The student will require excellent mathematics/statistics skills. Extrapolation requires good understanding of disease natural history and mechanisms of action of treatments. The student will b required to communicate effectively with clinical experts to develop models that are clinically plausible.
This project is a collaboration with Peninsula Technology Assessment Group (PenTAG) in Exeter. The student will be expected to visit Exeter regularly during the PhD where they will have the opportunity to work a current HTA project at PenTAG to gain experience of methodology issues that arise in practice.


Guyot P et al. 2012. doi:10.1186/1471-2288-12-9.
Welton et al. 2010. DOI: 10.1002/jrsm.21
Hoyle, & Henley 2011. DOI: 10.1186/1471-2288-11-139
Lin & Henley 2016. doi: 10.1002/sim.7051.
Cooper et al 2011. DOI: 10.1016/j.jval.2010.09.001
Ades et al. 2010. doi: 10.1111/j.1524-4733.2010.00784.x

Created on Nov. 6, 2016, 5:56 a.m.

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