The role

This post provides an exciting opportunity for a talented statistician to contribute to the development of impactful statistical methodology and to healthcare policy. You will contribute to developing methods for Multilevel Network Meta-Regression (ML-NMR), combining evidence from multiple trials with mixtures of individual participant and aggregate data, in order to produce population-adjusted estimates of treatment efficacy. These methods are increasingly prevalent in health technology assessment, and are used by manufacturers making submissions to regulators such as the National Institute for Health and Care Excellence (NICE). You will apply this methodological work and gain expertise in wider evidence synthesis methods by joining the Bristol Technology Assessment Group (TAG), which is contracted by the NIHR to conduct independent reviews of healthcare technologies, supporting the NICE guidance committees and other policy makers. Bristol is a centre for methodological excellence in evidence synthesis and health technology assessment, having developed many of the methods used as standard practice in NICE technology appraisals.

What will you be doing?

You will contribute to methodological development of the ML-NMR framework, developing extensions to use information from subgroup analyses or regression coefficients, and to handle issues such as missing data and treatment switching. You will design and analyse comprehensive simulation studies to assess the performance of these methods using the University’s high performance computing cluster, and contribute to the development of R packages to implement these methods. You will work as part of a multidisciplinary team in the Bristol TAG to contribute to the review of technology appraisals, subjecting manufacturer submissions to rigorous critique of their statistical analyses and conducting analyses under a range of alternative assumptions. You will be provided with training and development to ensure you have the skills necessary for this role and to stay up to date with the latest methodologies. There will be opportunities for you to contribute to teaching and research proposals, and you will be encouraged and supported to develop your own research interests over the course of the post.

You should apply if
You have training and experience in statistical modelling, including likelihoods, generalised linear models, and fixed vs random effectsYou have experience of coding in at least one statistical language, such as R, Python, or StataYou have training or experience of meta-analysisYou have an interest in the development and application of advanced statistical methodsYou have an interest in health technology assessment and an enthusiasm to contribute to NICE technology appraisalsYou work well independently and as part of a multidisciplinary teamYou have a relevant Masters degreeYou have a relevant PhD (or working towards one), or equivalent research experience, if applying for Senior Research Associate