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Potential MSc Courses for Round 3 NIHR Pre-Doctoral Fellowship

Contents

The Pre-Doctoral Fellowship offers applicants who currently do not hold a Masters Degree the opportunity to undertake a fully funded MSc course in a relevant research methodology.
  
The list below contains the names and contact details of Health Economics and Medical Statistics MScs running in Round 3 of the Fellowship. It is not an exhaustive list and applicants may apply for other relevant courses as required. We aim to build a portfolio of potential Masters Courses round upon round, therefore this list will expand as the Fellowship continues.
 
For further information, please refer to the latest set of guidance notes listed on the NIHR website. 

Health Economics Masters

Contact NameOrganisationContact emailMSc TitleAreas able to support
Victoria Serra-Sastre City, University of London v.serra-sastre@city.ac.uk  City, University of London: Health Economics

Victoria Serra-Sastre is a Senior Lecturer at the Department of Economics at City University London. Her main research interests are health economics and applied microeconometrics focusing on technology diffusion of health care technologies.

Victoria particularly looks at the impact of technology on health outcomes, hospital performance and changes in workforce composition.

Luigi Siciliani


Andrew Jones

University of York luigi.siciliani@york.ac.uk
 
andrew.jones@york.ac.uk 
University of York: Health Economics

Luigi Siciliani is a Professor of Health Economics at the Department of Economics and Related Studies at the University of York, where he directs the MSc in Health Economics.

He has specialised in health economics and micro-econometrics with a focus on healthcare providers. His research interests include waiting times for non-emergency treatment, hospital quality competition, contracting theory applied to health care, pay for performance and coordination between health and social care.

Andrew Jones is Professor of Economics at the University of York. He undertakes research in microeconometrics and health economics with particular interests in the determinants of health, the economics of addiction and socioeconomic inequalities in health and health care.

Louise Jackson University of Birmingham L.Jackson.1@bham.ac.uk
 
healtheconomics@contacts.bham.ac.uk 
University of Birmingham: MSc Health Economics and Health Policy

University of Birmingham: MSc Health Economics and Econometrics

We run two MSc programmes: MSc Health Economics and Health Policy and MSc Health Economics and Econometrics. There are also a number of modules that are available as short courses: Introduction to Health Economics; Economic Evaluation of Healthcare; Statistics for Health Economics Iⅈ Policy and Economics of Healthcare Delivery; Modelling for Health Economics. 

We are interested in supervising anything related to health economics and have specific expertise in methods modelling, econometrics and methods for valuing outcomes and dis-utilities associated with screening but have a broad team with specific expertise in a range of clinical areas and methods and who are happy to support a full range of Health Economics related research.

Dr Louise Jackson is a Senior Lecturer at the University of Birmingham. Louise has specific research interests in the area of economic evaluation and is particularly interested in methodological issues relating to the evaluation of public health and digital health interventions. Louise’s applied areas of interest include sexual health, obstetrics and gynaecology, women’s health and global health.

 

Medical Statistics Masters

Contact NameOrganisationContact emailMSc TitleAreas able to support
Stephanie Hubbard University of Leicester sjh62@le.ac.uk
 
mscmedstats@le.ac.uk 
University of Leicester: Medical Statistics

Stephanie Hubbard is the Associate Professor of Medical Statistics and joint course Director for the MSc in Medical Statistics. Her research interests are in the area of evidence synthesis for the evaluation of the effectiveness and cost-effectiveness of complex interventions, particularly in public health.

The Biostatistics and Genetic Epidemiology research group at Leicester delivers the MSc in Medical Statistics. Methods/areas in which we would be able to support fellows include: Survival Analysis; Health Technology Assessment; Health Economic Decision Modelling; Clinical Trials Methodology; Machine Learning; Analysis of linked Electronic Health Record data (“Big Data”); Causal Inference; Visualisation of Statistical Concepts, Data and Analyses results; and Statistical Methodology and Computation Development motivated by complex problems in genetics.

We have collaborations across many clinical and related disciplines with particularly strong collaboration in cancer survival, cardiovascular disease, public health and diabetes.

Dr Andrew Titman University of Lancaster a.titman@lancaster.ac.uk  University of Lancaster: Statistics Andrew Titman is Senior Lecturer in Statistics and PG Admissions officer for the Department of Mathematics and Statistics. His main research interests are in survival and event history analysis, with a particular interest in methodology and applications of multi-state models.

The Statistics group at Lancaster run the MSc in Statistics which includes a Medical pathway covering clinical trials, epidemiology, longitudinal data analysis and survival analysis.

We would be able to support fellowship applications in a broad range of areas including survival analysis, joint longitudinal-survival modelling, quality-of-life assessment, spatial epidemiology, adaptive design of clinical trials, personalised medicine, and health-care monitoring technology. We have active collaborations both with the university's Faculty of Health and Medicine and with external clinical trials units.
Kathy Baisley London School of Hygiene and Tropical Medicine (LSHTM) Kathy.Baisley@lshtm.ac.uk
 
mscmedstats@lshtm.ac.uk 
LSHTM: Medical Statistics
Kathy Baisley is the Associate Professor in Epidemiology and Medical Statistics. Her research areas include clinical trials, infectious disease policy and complex interventions.

Current areas of methodological research that the LSHTM Medical Statistics department would be able to support include: missing data, especially in longitudinal studies; propensity scores and other methods of adjustment for confounders; methods for causal inference (e.g. mediation analyses, methods for time-varying confounding adjustment); time-updated models relating disease events/biomarkers to prognosis; development of user-friendly prognostic risk scores; allowance for measurement error; small sample inference for mixed models.

Methodological research in clinical trials includes: adaptive designs; non-inferiority trials and surrogate endpoints; cross-over trials; multiplicity of data (e.g. subgroup analyses, composite endpoints, repeated measures) in trials; statistical methods for the evaluation of complex interventions.
 
Prof Daniel Stahl

Zahra Abdulla
King’s College London daniel.r.stahl@kcl.ac.uk KCL: Applied Statistical Modelling and Health Informatics
Daniel Stahl is Professor of Medical Statistics and Statistical Learning and lead of the "Precision Medicine and Statistical Learning" group. Zahra Abdulla is Senior Teaching fellow in statistics. They are academic program leads of the MSc in “Applied Statistical Modelling and Health Informatics, which is centred on the disciplinary strength and academic excellence of the Department of Biostatistics and Health Informatics located in the Institute of Psychiatry, Psychology and Neuroscience, King’s College London.

The MSc delivers a skill set and knowledge base in complex “multimodal” and “big data” analysis techniques, which are a recognised scarcity within UK Life sciences. Students will receive world-class training in core applied statistical methodology, machine learning and computational methodology. They will have the opportunity to apply their skills to real-life settings facilitated by the world-leading Institute of Psychiatry, Psychology & Neuroscience. Students can choose projects from a variety of research areas, such as health informatics, prediction modelling, clinical trials, causal modelling, psychometrics, epidemiology, structural equation modelling, natural language processing, machine learning, computational neuroscience and AI.