STATS 7056 - Biostatistics
North Terrace Campus - Semester 2 - 2018
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General Course Information
Course Details
Course Code STATS 7056 Course Biostatistics Coordinating Unit Mathematical Sciences Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Prerequisites MATHS 1012 Assumed Knowledge STATS 2107 Biennial Course Offered in even years Assessment ongoing assessment 30%, exam 70% Course Staff
Course Coordinator: Andrew Metcalfe
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
1. Demonstrate understanding of statistical issues arising in medical research.
2. Apply biostatistical knowledge to real-life problems in medical research.
3. Demonstrate skills in the design and analysis of clinical trials.
4. Demonstrate skills in the analysis of epidemiological data.
5. Ability to analyse biomedical data using R.
6. Demonstrate skills in interpreting and communicating the results of statistical analysis, orally and in writing.University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
All Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
All Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
2,3 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
1,2,3 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
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Learning Resources
Required Resources
None.Recommended Resources
Statistical methods in medical research (Fourth Edition). P.A. Armitage, G. Berry and J.N.S. Matthews, Blackwell, 2002.
An introduction to randomized controlled clinical trials (Second Edition). J.N.S. Matthews, CRC Press, 2006.
Statistics for epidemiology. N. Jewell, Chapman and Hall/CRC, 2004.Online Learning
This course uses MyUni for providing electronic resources, such as lecture notes, assignment papers, tutorial and computing exercises. Students should check their email and MyUni announcements for this course regularly for any notices or correspondence.
Link to MyUni login page:
https://myuni.adelaide.edu.au/webapps/login/ -
Learning & Teaching Activities
Learning & Teaching Modes
The lecturer guides the students through the course material in 24 lectures. Students are expected to prepare for lectures by reading the printed notes in advance of the lecture, and by engaging with the material in the lectures. Students are expected to attend all lectures, but lectures will be recorded to help with occasional absences and for revision purposes. In the fortnightly tutorials, students will discuss their solutions in groups and present them to the class on the board. These exercises will be further supplemented by the fortnightly computing practical sessions during which students will work under guidance on practical data analysis and develop computing skills using R. A series of five homework assignments builds on the tutorial and practical material and provides students with the opportunity to gauge their progress and understanding of the course material.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload Hours Lectures 24 72 Tutorials 6 18 Practicals 6 18 Assignments 5 48 Total 156 Learning Activities Summary
Lecture Outline
1. Introduction to epidemiology, clinical trials and randomization (lectures 1-3)
2. Design and analysis of clinical trials (lectures 4-14)
3. Statistical methods for epidemiology (lectures 15-20)
4. Statistical inference for 2x2 tables (lectures 21-24)
Tutorial Outline
1. HIV/AIDS case study
2. Methods of randomization
3. Sample size calculations
4. Group sequential trials
5. Crossover trials
6. Case-control studies and inference for 2x2 tables
Practical Outline
1. Biased coin designs and random permuted block designs in R
2. Permutation and bootstrap t-tests
3. Sample size calculations
4. Analysis of crossover trials
5. Epidemiological analysis of 2x2 tables
6. Tests for 2x2 tables -
Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
Component Assessment Mode Weighting Outcomes Assessed Tutorials Formative 5% All Practicals Formative 5% All Assignments Formative and Summative 20% All Exam Summative 70% All Assessment Related Requirements
An aggregrate final score of at least 50% is required to pass the course.Assessment Detail
Attendance at five out of six tutorials will contribute 5% to the assessment for this course, and attendance at five out of six computing practicals will contribute 5% to the assessment for this course, for a total of 10%. Tutorials will be in the odd weeks, commencing in Week 1. Computing practicals will be in the even weeks, commencing in Week 2. If students are unable to attend classes owing to illness or compassionate reasons, please let the lecturer know.
Assessment Item Distributed Due Date Weighting Assignment 1 Week 1 Week 3 4% Assignment 2 Week 3 Week 5 4% Assignment 3 Week 5 Week 7 4% Assignment 4 Week 7 Week 9 4% Assignment 5 Week 9 Week 12 4% Submission
All written assignments are to be submitted to the designated hand-in boxes within the School of Mathematical Sciences with a signed cover sheet attached.
Late assignments will not be accepted.
Assignments will have a two week turn-around time for feedback to students.Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through .
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Student Feedback
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.
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