STATS 7056 - Biostatistics

North Terrace Campus - Semester 2 - 2018

Biostatistics is fundamental to contemporary biomedical research. It plays a central role in evaluating new treatments for cancer and heart disease, in measuring survival following lung and liver transplants, in monitoring and predicting the spread of epidemics including HIV/AIDS and swine `flu, and much more. Biostatistics aldo emerged in recent years as a key collaborating discipline in bioinformatics following the sequencing of the human genome. You will learn that expert advice from biostatisticians is crucial for pharmaceutical drug development, health-data surveillance and analysis, and for informing government debate and health policy. This course provides an introduction to the design and analysis of clinical trials, epidemiological studies, and methods for the analysis of biostatistical data. Topics covered are: Clinical trials, Phase I to Phase IV trials, key aspects of study design: the Data and Safety Monitoring Board, trial types; justification of randomization, including ethical considerations; methods of randomization, unrestricted and restricted randomization, random permuted blocks, biased coin designs, stratification, minimization; randomization tests, permutation and bootstrap t-tests; calculating trial size, fixed and group sequential trials; power calculations for continuos and binary responses; more complex trial designs, crossover slinical trials and bioequivalence trials. Epidemiology; cohort, case-control and related observational studies; the advantages and disadvantages of each type of study; models for disease association, risk difference, relative risk, odds, ratio, attributable risk; the analysis of binary outcomes for retrospective and prospective data. Inference for 2x2 tables, the analysis of 2x2 tables and appropriate test procedures. Wald test, Likelihood Ratio test, profile likelihood; conditional inference for 2x2 tables; Fisher's Exact test: McNemar's test for matched pairs data; Mantel Haenszel test for comparing several 2x2 tables. Case studies on drug trials, heart disease, cancer, HIV/AIDS, leukaemia and environmental health.

  • 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 Course Planner.

  • 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)
    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
    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
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    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
  • Learning Resources
    Required Resources
    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:
  • 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.

    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:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. 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%
    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 Access Adelaide.

  • 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 ( 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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  • Policies & Guidelines
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