MSc
2022 start September 

Statistics with Medical Applications

School of Mathematics and Statistics, Faculty of Science

Learn how to apply advanced statistical techniques to solve problems in medicine and healthcare.
Woman looking through Statistics books

Course description

This course trains you to use statistical tools that are central to many areas of medicine: from clinical trials, to disease modelling, to measuring patient outcomes.

You’ll develop a detailed working knowledge of essential statistical techniques and concepts, including linear and generalised linear modelling, Bayesian statistics and computational methods and build up your programming and data analysis skills using the statistical computing software R.

You’ll study how these skills are applied in clinical trials and choose from a range of optional modules that focus on the role of statistics in other areas of medicine, such as epidemiology and evaluating healthcare interventions.

There are also optional modules on other topics in statistics, including time series analysis and machine learning.

Around one-third of the course is devoted to your dissertation on a medical or healthcare related topic. This may focus on investigating a data set or a more theoretical or methodological topic. The aim is to give you skills to include on your CV, such as planning and researching a project, data acquisition, problem specification, analysis and reporting your findings. Distance learning students often come with projects designed by their employer.

Accreditation

This course is accredited by the Royal Statistical Society.

Modules

The modules listed below are examples from the last academic year. There may be some changes before you start your course. For the very latest module information, check with the department directly.

Core modules:

The Statistician's Toolkit

This is the first of two ‘core’ modules students studying on statistics MScs. The aim of this module is to prepare statisticians for the workplace, equipping them with essential statistical modelling, computing and professional skills. The module includes the study of linear and generalised linear modelling, and data analysis using the programming language R.

30 credits
Bayesian Statistics and Computational Methods

This module introduces the Bayesian approach to statistical inference. The Bayesian method is fundamentally different in philosophy from conventional frequentist/classical inference, and has been the subject of some controversy in the past, but is now widely used. The module also presents various computational methods for implementing both Bayesian and frequentist inference, in situations where obtaining results ‘analytically’ would be impossible. The methods will be implemented using the programming languages R and Stan, and some programming is taught alongside the theory lectures.

30 credits
Medical Statistics

This module introduces an important application of statistics: medical research, specifically, the design and analysis of clinical trials. For any new drug to be approved by a regulator (such as the Medicines and Healthcare products Regulatory Agency in the UK) for use on patients, the effectiveness of the drug has to be demonstrated in a clinical trial. This module explains how clinical trials are designed and how statistical methods are used to analyse the results, with a particular focus on ‘survival’ or ‘time-to-event’ analysis.

15 credits
Dissertation (MSc Statistics with Medical Applications)

The dissertation is an extensive statistical study on a topic from a medical, pharmaceutical or health-related field. It gives the student the opportunity to synthesise theoretical knowledge with practical skills.

60 credits

Optional modules:

Economic Evaluation

The main aim of these modules is to encourage a broader appreciation of the complexities of economic evaluation through the examination of various decision making criteria, an assessment of alternative techniques for benefit measurement and the development of critical appraisal skills. A thorough grounding in the methods required to undertake an economic evaluation is developed, building on the introduction to economic evaluation given in HAR6200. The second part of the module is concerned with the measurement and valuation of costs and outcomes within economic evaluations.

15 credits
Epidemiology

Epidemiology is the discipline underpinning both effective public health practice and research into the causes, control and prevention of disease. Knowledge and understanding of epidemiological concepts and methods is a basic requirement for effective public health practice.

This module will provide an introduction to epidemiology covering key epidemiological concepts; measures of disease; association and causation; confounding and bias. It will also introduce research designs including cross-sectional, ecological, cohort, case-control and intervention studies and introduce population health measures such as screening.

15 credits
Systematic Reviews and Critical Appraisal Techniques

To familiarise students with principles of systematic reviews and critical appraisal and the acquisition of skills necessary to undertake such work. The unit includes an introduction to information systems; principles of systematic literature reviews and critical appraisal; search strategies; computer-assisted search methods; practicalities of writing up the results of a systematic review; introduction to Meta- analysis; and dissemination of findings.

15 credits
Qualitative Research Design and Analysis

On completing the module students will be expected to be able to: understand a range of qualitative research approaches, data collection methods and forms of analysis; plan and undertake a simple analysis of student-generated qualitative data; critically appraise the methods and results of qualitative research.

15 credits
Machine Learning

Machine learning lies at the interface between computer science and statistics. The aims of machine learning are to develop a set of tools for modelling and understanding complex data sets. It is an area developed recently in parallel between statistics and computer science. With the explosion of “Big Data”, statistical machine learning has become important in many fields, such as marketing, finance and business, as well as in science. The module focuses on the problem of training models to learn from training data to classify new examples of data.

15 credits
Sampling Theory and Design of Experiments

Whereas most statistics modules are concerned with the analysis of data, this module is focussed on the collection of data. In particular, this module considers how to collect data efficiently: how to ensure the quantities of interest can be estimated sufficiently accurately, using the smallest possible sample size. Three settings are considered: sample surveys (for example when conducting an opinion poll), physical experiments, as may be used in industry, and experiments involving predictions from computer models, where there is uncertainty in the computer model prediction.

15 credits
Time Series

This module considers the analysis of data in which the same quantity is observed repeatedly over time (e.g., recordings of the daily maximum temperature in a particular city, measured over months or years). Analysis of such data typically requires specialised methods, which account for the fact that successive observations are likely to be related. Various statistical models for analysing such data will be presented, as well as how to implement them using the programming language R.

15 credits
Economic Evaluation in International Health Technology Assessment DL

This unit introduces the concept of economic evaluation as applied to health technology assessment, the different types of evaluation that are available and the various stages and techniques that need to be applied to generate results. The techniques will be matched against current practice guidelines in different jurisdictions so that students can understand the current policy context of the methods. Also, as additional techniques are described, the strengths and weaknesses of each will be highlighted, with the students being encouraged to critically appraise their appropriateness to different contexts and jurisdictions.

15 credits
Systematic Reviews and Evidence Synthesis: Principles DL

The purpose of this unit is to enable students to understand the principles of systematic review. It covers the defining characteristics and application of systematic reviews; how to search for and select relevant evidence; how to quality assess primary research articles; the key issues that may generate bias; and the general principles governing evidence synthesis.

15 credits
Randomised Controlled Trials to Support Reimbursement Decision Making DL

Randomised controlled trials (RCTs) are of use when evaluating the effectiveness of interventions, as part of health technology assessment and decision-making. This unit covers the appropriate use of trials, the range of available research designs, strategies to optimise recruitment, sample size, outcome measures, and the use of economic and qualitative methods alongside trials.

15 credits
Cost-effectiveness Modelling in International Health Technology Assessment DL

This unit sets out the different uses of modelling in Health Technology Assessment (HTA), together with the main principles and common techniques (including Markov models and individual level simulation). It reviews the international guidance on good practice and develops particpants' skills to appraise and commission models for HTA. Learning is based around a core international literature on modelling covering methodological papers and a substantial set of case studies, ensuring learning is grounded in real world examples. Participants also undertake some simple 'hands-on' model-building and model reviewing in EXCEL to help embed the principles learned.Students interested to learn more about building cost-effectiveness models can enrol the Building Cost-effectiveness Models for HTA module (HAR6119). This module runs in the Spring semester and is worth 15 credits.

15 credits
Building Cost-effectiveness Models for Health Technology Assessment

An advanced course teaching the technical and practical skills required to build cost-effectiveness models for international health technology assessment. It builds on from the introductory unit, HAR6113 Cost-effectiveness modelling in international HTA, which covered the principles behind modelling. Content includes problem definition and structuring, building decision tree and Markov/state transition models in Excel, identifying appropriate data sources, implementing deterministic and probabilistic sensitivity analysis, and Value of Information analysis. It is an advanced course, aimed at students with a strong mathematical background who want to go on to build their own economic models.Students require a mathematical / statistical background and a sound understanding of the principles of modelling (prior completion of HAR6113 or evidence of previous training/knowledge). Good pre-existing Excel skills are essential. For students considering this Module as a standalone option who only require an understanding of the principles of economic modelling and good practice and who need the skills to be able to review other people¿s models, but do not need the technical skills to build their own models, the introductory module HAR6113: Cost-effectiveness Modelling in International HTA may be a more appropriate choice of module.

15 credits
Utility and Patient-reported Outcomes Data in Health Technology Assessment

The module explores the assessment and generation of patient reported outcomes for use in international health technology assessment, the quality adjusted life year (QALY) model and its weaknesses, and alternative methods of valuing benefits. The approaches used to generate outcomes and other utility data will be explored in relation to current practice guidelines of reimbursement and other agencies in different countries.

15 credits

The content of our courses is reviewed annually to make sure it's up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers. In the event of any change we'll consult and inform students in good time and take reasonable steps to minimise disruption. We are no longer offering unrestricted module choice. If your course included unrestricted modules, your department will provide a list of modules from their own and other subject areas that you can choose from.

Teaching

There are lectures, tutorials, computing sessions and group work. Most statistics lectures are recorded so you can watch them again later.

Distance learning option

This is taught online with support via email and an online forum.

You'll need to be in Sheffield for a few days between late May and early June each year for your exams. You're expected to spend around 20 hours each week on your studies if you're doing the two-year version of the course and around 12 to 15 hours each week if you're doing the three-year version.

Assessment

Some modules may be continuously assessed through ongoing project work with no examination, but most taught modules are assessed by a mixture of examinations and coursework. The assessment of the dissertation module is based entirely on your submitted dissertation.

Duration

  • 1 year, full-time
  • 2-3 years, part-time

Your career

This course is great training for statistician roles across medicine and healthcare. The skills students develop can be used to help bring new drugs to market in the pharmaceutical industry, design public health interventions to tackle national and international healthcare challenges or support clinicians on the frontline in the NHS.

This degree satisfies the eligibility criteria for the Royal Statistical Society’s Graduate Statistician award – a stepping-stone to full professional membership of the RSS and Chartered Statistician status.

Student profiles

Weishan Shi shares her experiences of studying the MSc Statistics with Medical Applications course in the School of Mathematics and Statistics.

Entry requirements

We ask for a 2:1 honours degree, or equivalent, with substantial mathematical and statistical components. In particular, you should have studied the following topics and performed well in assessments on them (for example, a score of at least 60 per cent).

  • Mathematical Methods for Statistics: ideas and techniques from real analysis and linear algebra, including multiple integration, differentiation, matrix algebra, the theory of quadratic forms.
  • Probability and Probability Distributions: the laws of probability and of conditional probability, the concepts of random variables and random vectors and their distributions, the methodology for calculating with them; laws of large numbers and central limit phenomena.
  • Basic Statistics: hypothesis testing; point estimation and confidence intervals; likelihood methods; linear modelling; use of statistical software, for example, R.

If you are not sure whether you are qualified for this course, download these sample exam papers. You should be able to answer most questions with a small amount of revision.

If you do not meet these entry requirements, we offer a Graduate Certificate in Statistics, which covers the topics listed above. Students who perform well on the Graduate Certificate can progress to the Statistics MSc or Statistics with Medical Applications MSc.

Before you start your MSc course, you should also read through the introductory material:

Overall IELTS score of 6.5 with a minimum of 6.0 in each component, or equivalent.

Pathway programme for international students

If you're an international student who does not meet the entry requirements for this course, you have the opportunity to apply for an International Foundation Year in Science and Engineering at the University of Sheffield International College. This course is designed to develop your English language and academic skills. Upon successful completion, you can progress to degree level study at the University of Sheffield.

We also accept a range of other UK qualifications and other EU/international qualifications.

If you have any questions about entry requirements, please contact the department.

Apply

You can apply for postgraduate study using our Postgraduate Online Application Form. It's a quick and easy process.

Apply now

Contact

postgradmaths-enquiry@shef.ac.uk
+44 114 222 3789

Any supervisors and research areas listed are indicative and may change before the start of the course.

Our student protection plan

Recognition of professional qualifications: from 1 January 2021, in order to have any UK professional qualifications recognised for work in an EU country across a number of regulated and other professions you need to apply to the host country for recognition. Read information from the UK government and the EU Regulated Professions Database.

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