STATS 3008 - Biostatistics III
North Terrace Campus - Semester 2 - 2018
General Course Information
Course Code STATS 3008 Course Biostatistics III Coordinating Unit School of Mathematical Sciences Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Prerequisites STATS 2107 or (MATHS 2201 and MATHS 2202) Assumed Knowledge Experience with the statistical package R such as would be obtained from STATS 1005 or STATS 2107 Course Description 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 has also 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 continuous and binary responses; more complex trial designs, crossover clinical 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 drugs trials, heart disease, cancer, HIV/AIDS, leukaemia and environmental health.
Course Coordinator: Andrew Metcalfe
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes1. 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
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
Recommended ResourcesStatistical 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 LearningThis 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 ModesThe 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 SummaryLecture 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)
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
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
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.
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 RequirementsAn aggregate final score of at least 50% is required to pass the course.
Assessment DetailAttendance 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.
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.
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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