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Statistics with Medical Applications
School of Mathematical and Physical Sciences ,
Faculty of Science
Course description
Our Statistics with Medical Applications MSc 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. You’ll build up your programming and data analysis skills using the statistical computing software R. You can also deepen your understanding of statistics with optional modules, such as time series analysis and machine learning.
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.
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. Distance learning students often come with projects designed by their employer.
You’ll gain skills to help you stand out in the graduate job market, such as planning and researching a project, data acquisition, problem specification, analysis and reporting your findings.
Recent examples of dissertation topics include:
- Modelling recruitment projection in clinical trials with application in trials conducted within the Sheffield Clinical Trials Research Unit
- Longitudinal analysis of outcomes in clinical trials
Accreditation
This course is accredited by the Royal Statistical Society.
Modules
Core modules:
- 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 - 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 - 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:
A student will take 15 credits (one module) from this group.
- 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.
15 credits
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. - Economic Evaluation
-
This module introduces the basic principles of economic evaluation as applied to healthcare interventions. The course introduces the concept of economic evaluation, the different types that are available and the various stages and techniques that need to be applied to generate results. Current practice guidelines will be described so that students can understand the current policy context of the methods. Also, as alternative techniques are described, their strengths and weaknesses will be highlighted, with the students being encouraged to critically appraise their appropriateness to different contexts.
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
Optional modules:
A student will take 30 credits from this group.
- 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 - 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 - Practical Aspects of Clinical Research
-
This unit is intended to run alongside the clinical research portfolio that is a part of the Masters course for NIHR Academic Clinical Fellows. It is also offered as one of the Core Modules for UK and overseas students on the generic MSc in Clinical Research. The course covers many of the practical and regulatory issues associated with carrying out clinical or health related research within a variety of national and international settings including the NHS. Face-to-face and web- based learning packages cover a variety of issues around research planning, project management, research governance, ethical and legal frameworks for research, good clinical practice, patient and public involvement, cultural competence, and dissemination and impact. The module runs across both semesters and students need to register for the Autumn semester and then continue into the Spring semester. There is no option to start in Spring and continue to the following Autumn semester.
30 credits
Year one – core modules:
- 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 - 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
Year one – optional modules:
A student will take 15 credits (one module) from this group.
- Cost-effectiveness Modelling in International Health Technology Assessment Online
-
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 participants' 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 on the Building Cost-effectiveness Models for HTA module (HAR6119). This module runs in the Spring semester and is worth 15 credits.
15 credits - Utility and Patient-reported Outcomes Data in Health Technology Assessment Online
-
The module explores the assessment and generation of patient reported outcome and utility measures for use in international health technology assessment including alternative valuation techniques, measures and underlying assumptions to the quality adjusted life year (QALY) model. 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. Measuring the wider societal benefits of interventions is also considered.
15 credits - Building Cost-effectiveness Models for Health Technology Assessment Online
-
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 - 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
Year two – core modules:
- 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 - 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
Year two – optional modules:
A student will take 15 credits from this group.
- Introduction to Research Methods Online
-
This module, which is delivered via online learning, provides students with an introduction to quantitative and qualitative research methods and to the types of skills necessary for the planning, data gathering and dissemination stages of health-related research. Topics covered include: -Research Design; -Methods of Data Collection; -Approaches to Analysing Data and Writing a Research Proposal. The course provides a foundation for further learning in specific research methods. It is primarily designed for new students beginning Masters courses in the Division of Population Health and there is an emphasis on research techniques used in public health and health services research (HSR).
15 credits - Systematic Approaches to Evidence Assessment Online
-
This module , which is delivered online, provides the skills and knowledge required to conduct rapid assessments of the evidence base and then to apply these assessments to health management and/or clinical practice. It introduces and explains rapid review methodologies that seek to optimise the balance between rigour and relevance, ensuring the timely and judicious use of evidence that is both user sensitive and context dependent. This module will also address the growing demand for knowledge translation of research findings, specifically within the context of developing countries. A key feature is production of a culturally-sensitive action plan that takes into account local feasibility and implementation issues.
15 credits - Epidemiology Online
-
As one of the core disciplines of Public Health, epidemiology is concerned with the methods of studying health related states and events to control health problems. The module, which is delivered online, covers epidemiological measures, main study designs, screening and surveillance. The primary objective is to introduce students to the principles, tools and applications of epidemiology and to help develop their practical skills through a range of activities.
15 credits - Systematic Reviews and Evidence Synthesis: Principles Online
-
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 - Economic Evaluation in International Health Technology Assessment Online
-
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 - Randomised Controlled Trials to Support Reimbursement Decision Making Online
-
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 - 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
Year two – optional modules:
A student will take 15 credits from this group.
- Cost-effectiveness Modelling in International Health Technology Assessment Online
-
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 participants' 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 on the Building Cost-effectiveness Models for HTA module (HAR6119). This module runs in the Spring semester and is worth 15 credits.
15 credits - Utility and Patient-reported Outcomes Data in Health Technology Assessment Online
-
The module explores the assessment and generation of patient reported outcome and utility measures for use in international health technology assessment including alternative valuation techniques, measures and underlying assumptions to the quality adjusted life year (QALY) model. 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. Measuring the wider societal benefits of interventions is also considered.
15 credits - Building Cost-effectiveness Models for Health Technology Assessment Online
-
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 - 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
Year one – core modules:
- 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 - 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
Year two – core modules:
- 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
Year two – optional modules:
A student will take 15 credits (one module) from this group.
- Introduction to Research Methods Online
-
This module, which is delivered via online learning, provides students with an introduction to quantitative and qualitative research methods and to the types of skills necessary for the planning, data gathering and dissemination stages of health-related research. Topics covered include: -Research Design; -Methods of Data Collection; -Approaches to Analysing Data and Writing a Research Proposal. The course provides a foundation for further learning in specific research methods. It is primarily designed for new students beginning Masters courses in the Division of Population Health and there is an emphasis on research techniques used in public health and health services research (HSR).
15 credits - Systematic Approaches to Evidence Assessment Online
-
This module , which is delivered online, provides the skills and knowledge required to conduct rapid assessments of the evidence base and then to apply these assessments to health management and/or clinical practice. It introduces and explains rapid review methodologies that seek to optimise the balance between rigour and relevance, ensuring the timely and judicious use of evidence that is both user sensitive and context dependent. This module will also address the growing demand for knowledge translation of research findings, specifically within the context of developing countries. A key feature is production of a culturally-sensitive action plan that takes into account local feasibility and implementation issues.
15 credits - Epidemiology Online
-
As one of the core disciplines of Public Health, epidemiology is concerned with the methods of studying health related states and events to control health problems. The module, which is delivered online, covers epidemiological measures, main study designs, screening and surveillance. The primary objective is to introduce students to the principles, tools and applications of epidemiology and to help develop their practical skills through a range of activities.
15 credits - Systematic Reviews and Evidence Synthesis: Principles Online
-
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 - Economic Evaluation in International Health Technology Assessment Online
-
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 - Randomised Controlled Trials to Support Reimbursement Decision Making Online
-
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 - 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 - Cost-effectiveness Modelling in International Health Technology Assessment Online
-
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 participants' 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 on the Building Cost-effectiveness Models for HTA module (HAR6119). This module runs in the Spring semester and is worth 15 credits.
15 credits - Utility and Patient-reported Outcomes Data in Health Technology Assessment Online
-
The module explores the assessment and generation of patient reported outcome and utility measures for use in international health technology assessment including alternative valuation techniques, measures and underlying assumptions to the quality adjusted life year (QALY) model. 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. Measuring the wider societal benefits of interventions is also considered.
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
Year three – core modules:
- 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
Year three – optional modules:
A student will take 15 credits (one module) from this group.
- Introduction to Research Methods Online
-
This module, which is delivered via online learning, provides students with an introduction to quantitative and qualitative research methods and to the types of skills necessary for the planning, data gathering and dissemination stages of health-related research. Topics covered include: -Research Design; -Methods of Data Collection; -Approaches to Analysing Data and Writing a Research Proposal. The course provides a foundation for further learning in specific research methods. It is primarily designed for new students beginning Masters courses in the Division of Population Health and there is an emphasis on research techniques used in public health and health services research (HSR).
15 credits - Systematic Approaches to Evidence Assessment Online
-
This module , which is delivered online, provides the skills and knowledge required to conduct rapid assessments of the evidence base and then to apply these assessments to health management and/or clinical practice. It introduces and explains rapid review methodologies that seek to optimise the balance between rigour and relevance, ensuring the timely and judicious use of evidence that is both user sensitive and context dependent. This module will also address the growing demand for knowledge translation of research findings, specifically within the context of developing countries. A key feature is production of a culturally-sensitive action plan that takes into account local feasibility and implementation issues.
15 credits - Epidemiology Online
-
As one of the core disciplines of Public Health, epidemiology is concerned with the methods of studying health related states and events to control health problems. The module, which is delivered online, covers epidemiological measures, main study designs, screening and surveillance. The primary objective is to introduce students to the principles, tools and applications of epidemiology and to help develop their practical skills through a range of activities.
15 credits - Systematic Reviews and Evidence Synthesis: Principles Online
-
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 - Economic Evaluation in International Health Technology Assessment Online
-
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 - Randomised Controlled Trials to Support Reimbursement Decision Making Online
-
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 - 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
Year three – optional modules:
A student will take 15 credits (one module) from this group.
- Cost-effectiveness Modelling in International Health Technology Assessment Online
-
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 participants' 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 on the Building Cost-effectiveness Models for HTA module (HAR6119). This module runs in the Spring semester and is worth 15 credits.
15 credits - Utility and Patient-reported Outcomes Data in Health Technology Assessment Online
-
The module explores the assessment and generation of patient reported outcome and utility measures for use in international health technology assessment including alternative valuation techniques, measures and underlying assumptions to the quality adjusted life year (QALY) model. 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. Measuring the wider societal benefits of interventions is also considered.
15 credits - Building Cost-effectiveness Models for Health Technology Assessment Online
-
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 - 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
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.
Open days
An open day gives you the best opportunity to hear first-hand from our current students and staff about our courses.
You may also be able to pre-book a department/school visit as part of a campus tour.Open days and campus tours
Duration
- 1 year, full-time
- 2-3 years, part-time
Teaching
You’ll be taught through lectures, tutorials, computing sessions and group work. Most statistics lectures are recorded so you can watch them again later.
You’ll be expected to spend around 35 hours each week on your studies, with 8-12 hours in lectures or computing classes, and the remainder consisting of independent study.
Distance learning option
Our distance learning option is taught online with support via email and an online forum. Distance learners also come to the University for residential weeks. 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
Our assessment methods are designed to support the achievement of learning outcomes and develop your professional skills. This includes ongoing project work for some modules, examinations, coursework and a dissertation.
Regular feedback is also provided, so you can understand your own development throughout the course.
Your career
Our Statistics with Medical Applications MSc is great training for statistician roles across medicine and healthcare. Our graduates develop the skills needed 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. Employers that have hired our graduates include AstraZeneca, GE Healthcare, GSK, the Medical Research Council, Public Health England and the NHS.
You’ll cover advanced topics and gain extensive research training, which is also great preparation for a PhD. Sheffield mathematics graduates have secured postgraduate research positions at many of the world's top 100 universities.
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.
School
School of Mathematical and Physical Sciences
The School of Mathematical and Physical Sciences is leading the way with groundbreaking research and innovative teaching.
Our mathematicians and statisticians have expertise across pure mathematics, applied mathematics, probability and statistics. We focus on a variety of topics, from the most abstract questions in algebraic geometry and number theory, to the calculations behind infectious disease, black holes and climate change.
In the Research Excellence Framework 2021, 96 per cent of our mathematical sciences research was rated in the highest two categories as world-leading or internationally excellent.
We have strong links with the Society for Industrial and Applied Mathematics, the Institute of Mathematics and its Applications, the European Physical Society, and the International Society on General Relativity and Gravitation. With the support of the London Mathematical Society, we are also an organiser of the Transpennine Topology Triangle, a key focal point for topology research in the UK.
Mathematics and statistics staff have received honours from the Royal Society, the Society for Mathematical Biology and the Royal Statistical Society, who also provide professional accreditation for our statistics courses.
Student profiles
Weishan Shi shares her experience of studying the MSc Statistics with Medical Applications course.
Entry requirements
Minimum 2:1 undergraduate honours degree in a relevant subject with relevant modules.
Subject requirements
We accept degrees in the following subject areas:
- Data Science
- Mathematics
- Statistics
We may consider other related degree subjects
Module requirements
You should have studied at least one module from the following areas:
Area 1: Mathematics
- Algebra / Linear Algebra
- Calculus
- Mathematics Methods
Area 2: Probability
- Markov chains/processes
- Probability theory/modelling
- Stochastic processes/models/modelling
Area 3: Statistics
- Applied statistics
- Bayesian statistics
- Computational statistics
- Data mining/analysis
- Econometrics
- Linear models / generalised linear models
- Medical statistics
- Multivariate statistics / multivariable statistics
- Non-parametric statistics
- Programming languages (e.g. R, Python)
- Sampling / survey design
- Statistical analysis/experiment/modelling
- Statistical software/computing
- Time series
Overall IELTS score of 6.5 with a minimum of 6.0 in each component, or equivalent.
If you have any questions about entry requirements, please contact the school/department.
Fees and funding
Apply
You can apply now using our Postgraduate Online Application Form. It's a quick and easy process.
Contact
Any supervisors and research areas listed are indicative and may change before the start of the course.
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.