HEDS is part of the School of Health and Related Research (ScHARR) at the University of Sheffield. We undertake research, teaching, training and consultancy on all aspects of health related decision science, with a particular emphasis on health economics, HTA and evidence synthesis.
Friday, 2 March 2018
PhD Opportunity at HEDS - Conducting indirect comparisons between treatments using disconnected evidence
ScHARR is currently advertising a project with the Healthcare Gateway scheme in collaboration with Amgen, supervised by HEDS academics Dr John Stevens and Dr Kate Ren:
If you are aware of any Home/EU students who may be eligible, please do encourage them to apply.
Regent Court - ScHARR
The decision to recommend interventions for use by the National Health Service in England is typically based on a health technology assessment that considers the relative costs and benefits associated with new and existing interventions. Health benefits are quantified using quality-adjusted life years, which involves estimation of the population mean survival for competing interventions.
When evidence is available about interventions evaluated in randomised controlled trials that share at least one treatment in common, relative effects can be estimated using network meta-analysis methodology. In some circumstances the evidence base may comprise patient-level data from singlearm or randomised controlled trials of a company’s intervention, and aggregate data from single-arm or randomised controlled trials of one or more comparator interventions but in which the trials do not share any interventions in common.
The aim of the research is to better understand statistical methods for assessing relative intervention effects on time-to- event outcomes associated with disconnected networks of evidence and to explore the assumptions behind the statistical methods. The intention is to build on the review by Stevens et al (2018) and NICE DSU Technical Support Document 18: Methods for Population-Adjusted Indirect Comparisons in Submissions to NICE.
The research questions will include:
How should indirect comparisons between interventions be made using disconnected evidence?
What is the extent of the systematic error arising from unaccounted for covariates when making unanchored comparisons using disconnected evidence?
How should the systematic error arising from unaccounted for covariates be adjusted for using disconnected evidence?
The research will focus primarily on statistical methods applicable to drug development in oncology in which interest is in estimating mean lifetime benefit i.e. involving the extrapolation of survival functions beyond the duration of an RCT and allowing for time- varying intervention effects.
It is envisaged that in the first year, the student would familiarise themselves with the design, analysis and reporting of oncology studies for drug registration and reimbursement purposes; the standard statistical literature on NMAs; the statistical literature on analysing patient-level observational data; and methods for populationadjusted indirect comparisons. In addition, the student would conduct a review of applications in the scientific literature and to reimbursement agencies of indirect comparisons using disconnected evidence.
In the second year, the student will develop and propose a method(s) for conducting indirect comparisons between interventions using disconnected evidence in the context of products for the treatment of cancer; the focus will be on the appropriate estimation of mean overall survival. The research will involve quantifying the extent of the systematic error arising from unaccounted for covariates when making unanchored comparisons using disconnected evidence and assessing the frequentist properties of the model using simulation methods. In addition, the use of Bayesian methods that allow the incorporation of external information to reflect parameter and structural uncertainty in addition to sampling variation will also be investigated.
In the third year, the student will complete the simulations, finalise recommendations, write manuscripts for publication in scientific journals, present the research at scientific conferences and complete the thesis.
Entry Requirements: Candidates must have a first or upper second class honours degree in Mathematics or Statistics, and a master’s degree in biostatistics or medical statistics, or significant research experience.