Programme Specification
A statement of the knowledge, understanding and skills that underpin a
taught programme of study leading to an award from
The University of Sheffield
1
Programme Title
Statistics with Medical Applications
2
Programme Code
MAST21, MAST22
3
JACS Code (if applicable)
G300
4
Level of Study
Postgraduate
5a
Final Qualification
MSc in Statistics with Medical Applications (MSc)
5b
QAA FHEQ Level
Masters
6a
Intermediate Qualification(s)
Postgraduate Diploma (PG Dip), Postgraduate Certificate (PG
Cert)
6b
QAA FHEQ Level
Masters
7
Teaching Institution (if not Sheffield)
Not applicable
8
Faculty
Science
9
Department
School of Mathematics and Statistics
10
Other Departments involved in
teaching the programme
ScHARR Statistics Group
11
Mode(s) of Attendance
Full-time (MAST21), Part-time (MAST22)
12
Duration of the Programme
1 year (MAST21), 2-4 years (MAST22)
13
Accrediting Professional or Statutory
Body
Royal Statistical Society
14
Date of production/revision
12/11/08, revised March 2012
15. Background to the programme and subject area
Statistics (the subject) lies at the heart of Medicine. From John Snow’s identification of the source of London
cholera in the 1850s and Doll’s & Hill’s pioneering work on the link between lung cancer and smoking in the 20 th
century to genomics, proteomics and modern clinical trials, statistical ideas and methods have been crucial in
many medical advances. More statisticians work in medicine, health and related fields than in any other area.
Nevertheless there is a shortage of well-qualified medical statisticians and therefore a substantial demand for
high-quality postgraduate training in this area, including demand for such training in part-time distance-learning
form.
The MSc in Statistics with Medical Applications provides both a practically-based professional training in the
application of statistics to medical and related areas, and a foundation for those wishing to pursue further
research. It is available via distance-learning (2-4 years, part-time) as well as by residential study (1 year fulltime). The programme is a development of that leading to the MSc in Statistics, which has been running
successfully for many years. It builds on the provision of a firm grounding in practical statistical methodology
and computation, including the development of the personal skills in demand by employers, from the established
Statistics MSc programme, and adds to them development of an understanding of, and ability to apply, the
concepts, models and tools of modern medical statistics. It provides an excellent foundation for a statistical
career in medical areas, or for further study for a research degree.
The established Statistics MSc programme has been supported by national Research Councils for over 40
years. In recent years it has been one of only around 6 Statistics MSc courses receiving EPSRC funding.
The MSc is accredited by the Royal Statistical Society. The Society accords GradStat status with one year's
relevant experience towards CStat status to all students who pass the course.
The programme is kept in close touch with the needs of employers through the programme's Advisory Board,
whose members are drawn from the pharmaceutical industry, medical organizations, commerce and
government. Students benefit from contacts with members of the Board, from meetings with employers through
open days, from career presentations and through work on dissertation projects on medical and health-related
topics.
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In the most recent national research assessment exercise (in 2008) the research of the Department of
Probability & Statistics received an overall grade profile of (10, 50, 30, 10) and that of the School of Health and
Related Research an overall grade profile of (20, 35, 35, 10), both signifying more than 50% of research in both
groups responsible for the MSc to be either of internationally excellent standard or world-leading. Students can
be confident that the training offered by the programme is informed by the latest thinking in the subject.
Further information is available from the School web site:
http://www.shef.ac.uk/maths/prospectivepg/taughtpg/pgstats/
16. Programme aims
The MSc aims:
(a) to provide a high-quality thorough initial training for professional statisticians with a strong interest in
medical, health and pharmaceutical applications;
(b) to provide an intellectual environment conducive to learning;
(c) to prepare students for careers which use their training in medical statistics;
(d) to provide teaching which is informed and inspired by the research and scholarship of the staff;
(e) to foster attitudes and confidence which will allow students to acquire new statistical knowledge
throughout a subsequent career
(f) to provide students with assessments of their achievements, and to identify and support academic
excellence
17. Programme learning outcomes
Knowledge and understanding:
Candidates for MSc, PG Dip and PG Cert will:
K1
have developed sound knowledge and understanding of relevant statistical theory and methodology
K2
understand the context of medical research, and the contribution of statisticians working in health
research, the health service and the pharmaceutical industry
K3
be able to formulate straightforward problems in statistical terms and analyse data using a range of
standard techniques
In addition, candidates for MSc and PG Dip will
K4
be able to formulate problems in statistical terms, select appropriate study designs to address questions
of medical relevance and analyse and interpret data using a range of standard techniques
In addition, candidates for MSc will:
K5
be able to formulate problems in statistical terms, select appropriate study designs to address questions
of medical relevance and analyze and interpret data by adapting and applying techniques as necessary
Skills and other attributes:
Candidates for MSc, PG Dip and PG Cert will:
S1
have facility in using at least one major statistical computer package, and general computer skills
S2
be able to conduct short statistical studies and have some experience of preparing reports
In addition, candidates for MSc and PG Dip will:
S3
have skill in the preparation and writing of longer reports (both technical and non-technical), in other
methods of communication of results (for example, oral presentation), and in working in groups
In addition, candidates for MSc will:
S4
have shown the ability to complete an extended individual study of a statistical problem in a medical,
pharmaceutical or health-related field and to present the results in a dissertation
S5
have skills to critically evaluate the use of statistical methods in the medical literature
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18. Teaching, learning and assessment
Development of the learning outcomes is promoted through the following teaching and learning
methods:
MAST21 is a full-time residential programme, with lectures, discussion sessions and computer laboratory
work. MAST22 is a part-time distance learning programme. The two programmes are as closely integrated
as possible within the constraints of their distinct identities. The distance learning version is designed so that
students study the same subjects as their residential counterparts at essentially the same times.
MOLE
The programme materials are made available through MOLE via the internet, and support for distant students
is available both from lecturers of individual modules, from fellow students and from a dedicated personal
tutor via email or telephone. Most communication within the programme, particularly between residential and
distance-learning students, takes place via MOLE and so training in its use is given early in the programme.
For all modules except the project-based Data Analysis module, students are provided with module notes,
structured problems and a schedule of work. The MOLE discussion board is the main vehicle for academic
interaction. It also serves to keep distance-learning students exactly in step with the delivery of material in
Sheffield. (K1-4, S1-3).
Independent Learning
This is the cornerstone of success in the programme. It is vital for the assimilation of the material provided, for
the preparation of written reports, and other presentations, and for the proper use of sophisticated software.
Residential Weeks
Distance learning students spend three or four residential weeks in Sheffield according as they take the MSc in
two years or three. The first of residential week is the Induction Week. During that week all students (distance
and residential) receive instruction in and gain initial experience of the main computer packages used in the
programme of study. They are also introduced to MOLE and its central role is explained. Basic, underpinning,
theoretical material is reviewed. (K2, S1)
Other residential weeks are held at the time of the examinations. Examinations take place towards the start of
the week and the later part of the week is used for group work and presentations. (S2-S3)
Distance learning students also have face-to-face meetings with their dissertation supervisors. (S4)
Lectures
A 20-credit lecture-module generally comprises about 40 lectures. Full printed lecture notes are made available.
The lectures themselves are used to explain and illustrate the most important points in the notes, with computer
demonstrations when appropriate. The MOLE discussion board is used to keep distance-learning students upto-date with what has been covered, and to highlight special points made during lectures. (K1-K4)
Problems
Students are required to submit work on specified problems at regular intervals for marking and feedback. (K1K4,S1)
Project and Assignment work
All modules require some practical work, designed to foster the integration of theory with practical skills. (S1-2).
However two modules have this aspect as their main focus. One requires the preparation of a number of
assignments designed to develop skills in statistical computing and the associated interpretation. (S1-2)
The other requires the preparation of a number of project reports based on real problems and data, often
originating from consulting activities, and for which a variety of approaches are likely to be illuminating. In
addition to gaining experience in the writing of reports, students gain experience in the use of other methods of
communication (seminar-style presentations, round table discussions, rôle-play) and in working with others on
larger projects in small groups. Groups involve both distance learning and residential students. Group
members use email and the discussion facilities of MOLE to collaborate, share documents, reach decisions and
prepare joint presentations. (K1-K3, S1-3)
Dissertation
Teaching for the dissertation is through individual supervision for each student by one or more members of
academic staff. Students will experience the key phases of a relatively large piece of work: planning to a
deadline; researching background information; acquisition and validation of data; problem specification; carrying
through relevant analyses; and reporting, both at length through the dissertation and in summary through a
poster display. Dissertation topics are mostly provided by medical colleagues or external organizations, and
learning to communicate with and relate to external clients expert in non-statistical areas is an extra benefit of
the dissertation. For distance learning students, appropriate projects based in the (medical, pharmaceutical or
health-related) workplace in co-operation with an employer are encouraged. (K1-K4, S2-4)
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Personal Tutorials
The School runs a personal tutorial system conforming to the guidelines in the University’s Students’ Charter.
The system is essentially pastoral; tutors are available to provide personal support and general academic
guidance.
Physical proximity
Residential students have a room with individual desks and an attached computer room. Distance learning
students share this space during residential weeks.
Opportunities to demonstrate achievement of the learning outcomes are provided through the following
assessment methods:
Assignments on statistical computing and the associated interpretation. K1, K3-K5, S1-2.
Project work associated with modules that also have an examination. K1-K2, S1-S3.
Other project work, singly and in groups, K1, K3-K5, S3.
Examinations, which are held in May/June, are 'open book', a format that encourages understanding rather than
learning by rote and provides an assessment of skills that are relevant to a working environment. K1-K5, S1, S5
Dissertation. S4 (and, as part of this, K1, K3-K5 and S3), S5.
19. Reference points
The learning outcomes have been developed to reflect the following points of reference:
Subject Benchmark Statements
http://www.qaa.ac.uk/AssuringStandardsAndQuality/subject-guidance/Pages/Subject-benchmarkstatements.aspx
Framework for Higher Education Qualifications (2008)
http://www.qaa.ac.uk/Publications/InformationAndGuidance/Pages/The-framework-for-higher-educationqualifications-in-England-Wales-and-Northern-Ireland.aspx
University Strategic Plan
http://www.sheffield.ac.uk/strategicplan
Learning and Teaching Strategy (2011-16)
http://www.shef.ac.uk/lets/staff/lts
The research interests and scholarship of the staff.
The QAA benchmark document at
http://www.qaa.ac.uk/Publications/InformationAndGuidance/Documents/Maths07.pdf with its MMath annexe at
http://www.qaa.ac.uk/Publications/InformationAndGuidance/Documents/MathsAnnex09.pdf
The European Mathematical Society Mathematics Tuning Group report “Towards a common framework for
Mathematics degrees in Europe” at
www.maths.soton.ac.uk/EMIS/newsletter/newsletter45.pdf pages 26-28.
(The Royal Statistical Society’s accreditation framework
http://www.rss.org.uk/site/cms/contentCategoryView.asp?category=292
Contacts with employers, mainly through the programme's Advisory Board
The University of Sheffield Students’ Charter at
www.shef.ac.uk/ssid/ourcommitment/charter
The University’s coat of arms, containing the inscriptions Disce Doce (Learn and Teach) and Rerum
Cognoscere Causas (To Discover the Causes of Things; from Virgil's Georgics II, 490), at
www.shef.ac.uk/about/arms.html
20. Programme structure and regulations
The full-time (residential) and part-time (distant learning) programmes begin together with an induction week in
Sheffield in September. The full-time course is offered over 12 months, finishing in the following September. The
part-time course takes 2 or 3 (or exceptionally 4) years to complete. The components other than the
dissertation must be completed within three years.
The teaching year is divided into two semesters each of fifteen weeks. Modules to the value of 120 credits must
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be taken during this period. The six main modules are each of 20 credits and run through both semesters. Some
flexibility is allowed in the programme by the provision of some one-semester 10-credit reading modules.
Examinations for four of the 20-credit modules are held in May-June; the exceptions are MAS6001 and
MAS6002, which, together with the 10-credit modules, are assessed entirely by coursework.
All students must take:
MAS6061 Epidemiology & Time Series (20 credits)
MAS6062 Bayesian Methods and Clinical Trials (20 credits)
MAS6001 Data Analysis (20 credits)
MAS6002 Statistical Laboratory (20 credits)
All students take further modules:
MAS6003 Linear Modelling (20 credits)
MAS6012 Medical Statistics, Sampling and Design (20 credits)
except when there is compelling evidence of existing competence based on previous qualifications. In this case
two 10-credit modules on Special Topics may replace one of the modules above. The Special Topics options
are given as individual guided reading units and allow for study to greater depth in, for example, multivariate
analysis, time series, simulation, or non-parametric and semi-parametric methods.
All students complete a Dissertation (60 credits) on a topic from a medical, pharmaceutical or health-related
field.
Part-time students who take the modules (other than the dissertation) over two years normally take ‘Statistical
Laboratory’, ‘Epidemiology & Time Series’ and ‘Linear Modelling’ in year 1 and ‘Data Analysis’, ‘Bayesian
Methods and Clinical Trials’ and ‘Medical Statistics, Sampling and Design’ in year 2. Those who take the
modules (other than the dissertation) over three years take ‘Statistical Laboratory’ and ‘Linear Modelling’ in year
1, ‘Data Analysis’ and ‘Medical Statistics, Sampling and Design’ in year 2 and ‘Bayesian Methods and Clinical
Trials’ and ‘Epidemiology & Time Series’ in year 3.
Residential students begin work on the dissertation in early Spring, but work on it most intensively during the
Summer. The arrangement for part-time students is more flexible, but they too are expected to do most of the
work during the summers or in the year after they have completed all the other modules.
Successful completion of the programme leads to the award of the MSc with either ‘pass’, ‘pass with merit’ or
‘pass with distinction’ grade.
The PG Diploma is available for candidates who take all of the taught part of the MSc but not the dissertation.
The PG Certificate is available for candidates who take only a sub-set of the modules and do not undertake the
dissertation.
Detailed information about the structure of programmes, regulations concerning assessment and progression
and descriptions of individual modules are published in the University Calendar available on-line at
http://www.shef.ac.uk/govern/calendar/regs.html.
21. Student development over the course of study
The compulsory modules provide training in the basic ideas of epidemiological methods and time series
modelling (Epidemiology & Time Series). They also introduce modern ideas from inference and the design and
analysis of clinical trials (Bayesian Methods and Clinical Trials), and give the student the opportunity to develop
the professional skills required to tackle substantial statistical projects and to communicate results. The
Statistical Laboratory module introduces and develops practical skills that are drawn on and used in all the other
modules and in the dissertation. The focus of Data Analysis is the preparation and communication of reports on
practical statistical problems. In both modules the tasks, on which feedback is given as the module develops,
become more challenging through the year as student skills develop. Data Analysis is also the vehicle for
general professional development, including the opportunity to extend communication skills, to experience group
working, to develop the interpersonal skills needed in statistical consultancy, and to appreciate the ethical
framework of professional activities. Work on medical applications progresses from basic ideas founded on
relative risk and odds ratios derived from simple prospective and retrospective studies in Epidemiology to the
study of more advanced designs in Clinical Trials and to model-based analyses and powerful computational
methods in Bayesian Methods and in Medical Statistics.
The dissertation draws on and extends the knowledge and skills acquired in other parts of the programme, and
promotes the development of independent and reflective modes of study.
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22. Criteria for admission to the programme
Detailed information regarding admission to the MSc programmes is available from the P&S web site at
www.shef.ac.uk/study
23. Additional information
There is an active local group of the Royal Statistical Society in Sheffield which organises a series of meetings
through the year featuring visiting national speakers. The talks are accessible to and interesting for students on
this programme.
This specification represents a concise statement about the main features of the programme and should be
considered alongside other sources of information provided by the teaching department(s) and the University. In
addition to programme specific information, further information about studying at The University of Sheffield can
be accessed via our Student Services web site at http://www.shef.ac.uk/ssid.
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