ࡱ>  0Gbjbjvv 7j1gj1g>3" " EEEYYY8\dY7=Q?UUU*&'TT',<<<<<<<$?Ab<E'&"*&''<UU<///'8UEU</'<//;|-n<U3l2(6J<<=07=\<+Bh):+B$n<+BEn<4''/'''''<<,:'''7=''''+B'''''''''" :   1Programme TitleStatistics with Medical Applications2Programme CodeMAST21, MAST223JACS CodeG300, G3114Level of StudyPostgraduate5aFinal QualificationMSc in Statistics with Medical Applications (MSc)5bQAA FHEQ LevelMasters6aIntermediate Qualification(s)Postgraduate Diploma (PG Dip), Postgraduate Certificate (PG Cert)6bQAA FHEQ LevelL77Teaching Institution (if not «Ӱҵ)Not applicable8FacultyScience9DepartmentSchool of Mathematics and Statistics10Other Departments involved in teaching the programmeScHARR11Mode(s) of AttendanceFull-time (MAST21), Part-time (MAST22)12Duration of the Programme1 year (MAST21), 2 years (MAST22)13Accrediting Professional or Statutory BodyRoyal Statistical Society14Date of production/revisionNovember 2008, revised March 2012, March 2016, April 202115. Background to the programme and subject area Statistics (the subject) lies at the heart of Medicine. From John Snows identification of the source of London cholera in the 1850s and Dolls & Hills pioneering work on the link between lung cancer and smoking in the 20th 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 an alternative to 1 year full-time study.. 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 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.16. Programme aims The MSc aims to: provide a high-quality thorough initial training for professional statisticians with a strong interest in medical, health and pharmaceutical applications; provide an intellectual environment conducive to learning; prepare students for careers which use their training in medical statistics; provide teaching which is informed and inspired by the research and scholarship of the staff; foster attitudes and confidence which will allow students to acquire new statistical knowledge throughout a subsequent career; provide students with assessments of their achievements, and to identify and support academic excellence.17. Programme learning outcomes Knowledge and understanding: On successful completion of the programme, students will be able to demonstrate knowledge and understanding of:K1statistical theory, including Bayesian and frequentist approaches for statistical inference;K2a wide variety of statistical modelling techniques;K3computational methods for implementing statistical analyses;K4at least one statistical computing language;K5the application of statistical methods in medical research. Skills and other attributes: On successful completion of the programme, students will be able to:S1identify and implement an appropriate statistical modelling method, when presented with a data analysis problem, including data analysis problems in clinical trials;S2produce written reports which describe statistical analyses and present the findings;S3use appropriate software for analysing data, implementing statistical modelling methods, and preparing written reports;S4communicate the results of statistical analyses to non-expert audiences;S5plan and complete an extended individual study of a statistical problem and to present the results in a dissertation.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, MAST22 is a part-time distance learning programme. They are as closely integrated as possible within the constraints this difference imposes. The distance learning version is designed so that students study the same subjects as students on the full time version. Blackboard The course materials are made available through Blackboard and support is available from a designated personal tutor, from the individual module lecturers and from the course's Course Director via email or telephone. Most communication within the course takes place via Blackboard and so training in its use is given early in the MSc. For all modules (except the dissertation module) students are provided with study materials, structured problems and a schedule of work. The Blackboard 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 «Ӱҵ. (K1-K5). 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 (K1-K5). Lectures A 15-credit lecture-module generally comprises about 20 lectures. In lectures the important points in the lecture notes are explained and illustrated, with computer demonstrations when appropriate. The Blackboard discussion board is used to keep distance-learning students up-to-date with what has been covered and highlight any special points made during lectures. (K1-5, S1, S3). Some lectures will include discussion and demonstration of presenting statistical concepts to non-expert audiences (S4). Computing classes A number of classes are held in computer labs, where students can learn and practise statistical computing. Demonstrators are available in the classes to help students with any questions or problems (K3, K4, S3). Formative assessment All modules (with the exception of the dissertation module) include non-assessed exercises. Students can hand in their work and receive feedback on their solutions. Where appropriate, model solutions are provided for these exercises (K1, K2, S1). Project work and feedback Students will complete a number of projects over the MSc, and will receive feedback on both technical aspects particular to the project, and presentation themes that are common to all projects (S1-S4). Dissertation Teaching for the dissertation is through supervision by one or more members of School of Health and Related Research. The dissertation project is on a medical topic. 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 out relevant analyses; and reporting at length through the dissertation. Dissertation topics may be provided by external clients, and learning to communicate with, and relate to, these clients is an extra benefit of the dissertation; for distance learning students, projects based in the workplace in co-operation with an employer are encouraged. (K1-K5, S4, S5). Personal Tutorials The Department runs a personal tutorial system conforming to the guidelines in the Universitys Students Charter. The system is essentially pastoral; tutors are available to provide personal support and general academic guidance. Opportunities to demonstrate achievement of the learning outcomes are provided through the following assessment methods: Project work, requiring report-writing and statistical computing K1-K5, S1-S4. K1, SK1, S1-2. Examinations, which are held in May/June, K1-K3, K5, S1. Dissertation K1-K5, S1, S3-5.19. Reference points Subject Benchmark Statements  HYPERLINK "https://www.qaa.ac.uk/quality-code/subject-benchmark-statements" https://www.qaa.ac.uk/quality-code/subject-benchmark-statements Framework for Higher Education Qualifications (2014)  HYPERLINK "https://www.qaa.ac.uk/docs/qaa/quality-code/qualifications-frameworks.pdf" https://www.qaa.ac.uk/docs/qaa/quality-code/qualifications-frameworks.pdf University Vision  HYPERLINK "/vision" /vision Learning and Teaching Strategy (2016-21)  HYPERLINK "/polopoly_fs/1.661828!/file/FinalStrategy.pdf" /polopoly_fs/1.661828!/file/FinalStrategy.pdf The research interests and scholarship of the staff. The Royal Statistical Societys accreditation framework. Contacts with employers, mainly through the programme's Advisory Board «Ӱҵ Students Charter at  HYPERLINK "http://www.shef.ac.uk/ssid/ourcommitment/charter/" \h http://www.shef.ac.uk/ssid/ourcommitment/charter/ The Universitys 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 HYPERLINK "http://www.sheffield.ac.uk/about/arms" \h   HYPERLINK "http://www.sheffield.ac.uk/about/arms" \h http://www.sheffield.ac.uk/about/arms20. Programme structure and regulations All students must take: MAS61004 The Statisticians Toolkit. MAS61006 Bayesian Statistics and Computational Methods. MAS61002 Medical Statistics and will complete a 60 credit dissertation. Students on MAST21 will take at least one of HAR6260 Economic Evaluation. HAR619 Epidemiology. HAR650 Systematic Reviews and Critical Appraisal Techniques. HAR6531 Qualitative Research Design and Analysis. and at most two of MAS61007 Machine Learning. MAS61003 Sampling Theory and Design of Experiments. MAS61005 Time Series. With the permission of both the course director and the modules leaders, up to 45 credits from the lists of optional modules may be replaced by: HAR6115 Economic Evaluation in International Health Technology Assessment. HAR6114 Systematic Reviews and Evidence Synthesis Principles. HAR6116 Randomised Controlled Trials to Support Reimbursement Decision Making. HAR6113 Cost Effective Modelling in International Health Technology Development. HAR6119 Building Cost Effective Models for Health Technology Assessment. HAR6118 Utility and Patient-reported Outcomes Data in HTA. Students on MAST22 can choose three modules from MAS61003 Sampling Theory and Design of Experiments. MAS61005 Time Series. MAS61007 Machine Learning. HAR6113 Cost Effective Modelling in International Health Technology Development. HAR6118 Utility and Patient-reported Outcomes Data in HTA. HAR6044 Systematic Approaches to Evidence Assessment. HAR6048 Epidemiology. HAR6116 Randomised Controlled Trials to Support Reimbursement Decision Making. HAR6043 Research Methods. HAR6115 Economic Evaluation in International Health Technology Assessment. HAR6114 Systematic Reviews and Evidence Synthesis Principles. HAR6113 Cost Effective Modelling in International Health Technology Development. HAR6118 Utility and Patient-reported Outcomes Data in HTA. HAR6119 Building Cost Effective Models for Health Technology Assessment. Subject to appropriate balancing of modules across semesters and years of study. For full time students the dissertation is mainly prepared during the summer. The arrangement for part-time students is more flexible but it is expected that they too will 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. 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  HYPERLINK "http://www.shef.ac.uk/govern/calendar/regs.html" \h http://www.shef.ac.uk/govern/calendar/regs.html.21. Student development over the course of study The three compulsory modules (MAS61002, MAS61004 and MAS61006) form a core, and will equip students with essential modelling, computational and professional skills, as well as specialist knowledge in medical statistics. These will include linear and generalised linear modelling, exploratory data analysis and statistical computing using R, Bayesian inference and Monte Carlo methods, and presentation skills. Students will have the knowledge and confidence to tackle a wide range of data analysis problems, and, when confronted with non-standard problems, make use of computational methods and/or simple exploratory approaches as appropriate. The remaining modules enable the students to develop specialised knowledge of particular topics in both statistics and in medical statistics, as well as further developing their professional skills in communication and report-writing. The dissertation draws on the knowledge and skills acquired in the remainder of the programme.22. Criteria for admission to the programme The minimum entrance requirement for the course is: either a Second Class Honours Degree, from a three or four year course at a UK university, with substantial mathematical and statistical components; or any comparable qualification of equivalent standard. The School also offers a Graduate Certificate which can be used as an entry qualification for the programme. In addition, students whose first language is not English will need to demonstrate English language proficiency (even if their education has been chiefly in English). Our usual minimum requirements are: TOEFL 232 (computer-based) or 575 (paper-based), IELTS 6.5, or equivalent. Detailed information regarding admission to the programme is available at HYPERLINK "http://www.shef.ac.uk/study/" \h   HYPERLINK "http://www.shef.ac.uk/study/" \h http://www.shef.ac.uk/study/Detailed information regarding admission to programmes is available from the Universitys On-Line Prospectus at HYPERLINK "http://www.shef.ac.uk/courses/" \h   HYPERLINK "http://www.shef.ac.uk/courses/" \h http://www.shef.ac.uk/courses/23. Additional information There is an active local group of the Royal Statistical Society in «Ӱҵ 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 «Ӱҵ can be accessed via our Student Services web site at  HYPERLINK "http://www.shef.ac.uk/ssid" \h http://www.shef.ac.uk/ssid.     mast22 ver21-22 PAGE1 Programme Specification A statement of the knowledge, understanding and skills that underpin a taught programme of study leading to an award from «Ӱҵ :;<=>LM[\]^_histuvw  [ \ ] _ ` n o q r s t u ȽȽȽȽȽȽȽȽȽȽȽȽȽȽȽȽȽȲ hKQhSHhKQhSHB*phhKQhrB*phhKQhSH5B*ph hKQhr#jhM hM 5UmHnHuhrjhKQUmHnHuA;;(($$d%d&d'd(dIfNOPQR`gdKQ $<`a$gdKQ4d$d%d&d'd(d1$NOPQR`;<>M\e)));(($$d%d&d'd(dIfNOPQR`gdKQkdhS$$IfHF'@{0`'    44 HapytM \]_ite)));(($$d%d&d'd(dIfNOPQR`gdKQkd T$$IfHF'@{0`'    44 HapytM tuwe)));(($$d%d&d'd(dIfNOPQR`gdKQkdT$$IfHF'@{0`'    44 HapytM e)));(($$d%d&d'd(dIfNOPQR`gdKQkdU$$IfHF'@{0`'    44 HapytM e)));(($$d%d&d'd(dIfNOPQR`gdKQkdHV$$IfHF'@{0`'    44 HapytM  \ e)));(($$d%d&d'd(dIfNOPQR`gdKQkdW$$IfHF'@{0`'    44 HapytM \ ] ` o r e)));(($$d%d&d'd(dIfNOPQR`gdKQkdW$$IfHF'@{0`'    44 HapytM r s u e)));(($$d%d&d'd(dIfNOPQR`gdKQkdpX$$IfHF'@{0`'    44 HapytM  * + 1 2 3 5 6 K L r s t v w   J U V W g i T U 12222عƱƭhKQhKQhSHH*hKQhr5B*ph hKQhSHhKQhSHB*phhKQhSH5B*ph hKQhrhKQhrB*phhKQB*phD e)));(($$d%d&d'd(dIfNOPQR`gdKQkd(Y$$IfHF'@{0`'    44 HapytM  e)));(($$d%d&d'd(dIfNOPQR`gdKQkdY$$IfHF'@{0`'    44 HapytM  + 2 e)));(($$d%d&d'd(dIfNOPQR`gdKQkdZ$$IfHF'@{0`'    44 HapytM 2 3 6 L s e)));(($$d%d&d'd(dIfNOPQR`gdKQkdP[$$IfHF'@{0`'    44 HapytM s t w e)));(($$d%d&d'd(dIfNOPQR`gdKQkd\$$IfHF'@{0`'    44 HapytM  e)));(($$d%d&d'd(dIfNOPQR`gdKQkd\$$IfHF'@{0`'    44 HapytM   V e)));(($$d%d&d'd(dIfNOPQR`gdKQkdx]$$IfHF'@{0`'    44 HapytM V W e$A$ @ xx$d%d&d'd(dNOPQR`gdKQkd0^$$IfHF'@{0`'    44 HapytM  2DEcjkd^$$IfH'`'0`'44 Hap ytM Px$If`gdKQP$If`gdKQ$x$If`a$gdKQ222CDEWXghi>?ihiZ[\^_/013434578hKQhr5hKQhSH5hKQhr5B*phhKQhSH5B*ph hKQhSH hKQhrOEXi?iv$ & Fxx$If^`a$gdKQ$ & Fx$If^`a$gdKQ$x$If`a$gdKQA$ @ xx$d%d&d'd(dNOPQR`gdKQTCCPP$If`gdKQA$ @ xx$d%d&d'd(dNOPQR`gdKQjkdo_$$IfH'`'0`'44 Hap ytM F_kd]`$$IfH0'@ %`'244 HapytM (($If`gdKQGkd_$$IfH'`'`'244 Hap ytM [\_._kdYa$$IfH0'@ %`'244 HapytM (($If`gdKQ_kd`$$IfH0'@ %`'244 HapytM _._kdUb$$IfH0'@ %`'244 HapytM _kda$$IfH0'@ %`'244 HapytM (($If`gdKQ014(($If`gdKQGkdb$$IfH'`'`'244 Hap ytM PP$If`gdKQ`gdKQ458._kdc$$IfH0'@ %`'244 HapytM (($If`gdKQ_kd?c$$IfH0'@ %`'244 HapytM 81vwxFGQRRR  !"""#######$$$$$%%%%%&'(((((()))hKQhKQhr5CJaJhKQhSH5CJaJhKQhr5B*phhKQhSH5B*phhKQhr5hKQhSH5 hKQhr hKQhSHD8._kdd$$IfH0'@ %`'244 HapytM _kd;d$$IfH0'@ %`'244 HapytM (($If`gdKQwxM;$x$If`a$gdKQA$ @ xx$d%d&d'd(dNOPQR`gdKQ_kd7e$$IfH0'@ %`'244 HapytM (($If`gdKQGR "###$$%%(($x$If`a$gdKQ$$If`a$gdKQ$$If]`a$gdKQ$$x$If`a$gdKQ$x$If]`a$gdKQ()))'*zq79x$$d%d&d'd(dIfNOPQR`gdKQ`gdKQskde$$IfH'`'    0`'2d2 P44 Hap ytM $x$If`a$gdKQ)))&*'***********+++]+^+++++++,,-,v,w,x,,,,,,,,ƾƾƒjj-h9hKQ0J!B*CJOJQJ^JaJphU h9hKQCJOJQJ^JaJ)h9hKQB*CJOJQJ^JaJphjhKQ0J!Uh9hKQ0J!hKQjhKQU h9hKQhKQhr5B*ph hKQhSHhKQhrB*phhKQhSH5B*ph hKQhr%'*****8jkd\f$$IfH'`'0`'44 Hap ytM ;xx$$d%d&d'd(dIfNOPQR`gdKQx$If`gdKQ$x$If`a$gdKQ**+++x,,, ---m$x$If`a$gdKQ x$IfgdKQ"x$If[$\$gdKQ"x$If[$\$gdKQ $IfgdKQ x$IfgdKQA$ @ xx$d%d&d'd(dNOPQR`gdKQ ,,,- - 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