Biostatistics
Graduate Certificate in Biostatistics
(GradCertBiostat) KG003
Graduate Diploma in Biostatistics
(GradDipBiostat) KF034
Master of Biostatistics
(MBiostat) KC044
| Course | Credit points for award | Duration part-time |
|---|---|---|
| GradCertBiostat | 24 | 1 to 2 years |
| GradDipBiostat | 48 | 2 to 4 years |
| MBiostat | 72 | 3 to 6 years |
Overview
Biostatistics is the application of statistical techniques to scientific research in health-related fields, including medicine and public health. In recent times, the results of biostatistical research have become pivotal in improving health and reducing illness. Biostatisticians play essential roles in designing studies, analysing data and creating methods to solve research problems. These courses have been designed to provide advanced biostatistical training for a diverse range of students and are delivered by distance learning.
Course Information
The program is delivered predominantly via distance learning (electronically and by mail). It is taught by a group of senior academic biostatisticians based in universities around Australia.
The only units of study not available via distance learning are PUBH5215 Introductory Analysis of Linked Data, and the Part 4 workplace projects, for which students must be supervised by a biostatistician approved by the University of Sydney.
Assessment for most coursework subjects is by assignment only, although some units of study may have a take-home or online exam.
It is recommended that students undertake no more than two units of study per semester. Students should contact the program coordinator for advice on how best to structure their program of study, taking into account the prerequisites.
Students may apply for a waiver for one or more of BSTA5001, BSTA5002, BSTA5011, BSTA5023 depending on the student€™s previous studies. Students granted a waiver for these units of study must choose a unit from Part 3 of the Table of Units of Study: Biostatistics to make up the required credit points.
Graduate diploma students, with no waivers, must complete all units of study from Part 2 of Table, except BSTA5009.
Degree Resolutions
Sydney Medical School resolutions and the printed handbook are the official statement of Faculty policy. The resolutions contained in the printed handbook are accurate as at November 2010. If a conflict is perceived between the content of the printed handbook and information available elsewhere, Sydney Medical School resolutions and the information available in the handbook online shall always take precedence. See the handbook online website:
http://sydney.edu.au/handbooks/handbooks_admin/medicine.shtml.
See the Policy Online website: http://sydney.edu.au/policy, for copies of University policies.
Master of Biostatistics
These resolutions must be read in conjunction with applicable University By-laws, Rules and policies including (but not limited to) the University of Sydney (Coursework) Rule 2000 (the 'Coursework Rule'), the Resolutions of the Faculty, the University of Sydney (Student Appeals against Academic Decisions) Rule 2006 (as amended) and the Academic Board policies on Academic Dishonesty and Plagiarism.
Course resolutions
1 Course codes
| Code | Course title |
|---|---|
| KG003 | Graduate Certificate in Biostatistics |
| KF034 | Graduate Diploma in Biostatistics |
| KC044 | Master of Biostatistics |
2 Attendance pattern
3 Master's type
0.
The master's degree in these resolutions is a professional master's course as defined by the Coursework Rule.
4 Embedded courses in this sequence
(2)
Providing candidates satisfy the admission requirements for each stage, a candidate may progress to the award of any of the courses in this sequence. Only the longest award completed will be conferred.
5 Admission to candidature
(1)
Available places will be offered to qualified applicants based on merit, according to the following admissions criteria. In exceptional circumstances the Dean may admit applicants without these qualifications who, in the opinion of the Faculty, have qualifications, evidence of experience and achievement sufficient to successfully undertake the award.
(a)
a bachelor's degree in statistics, mathematics, science, psychology, medicine, pharmacy, economics, health sciences or other appropriate discipline from the University of Sydney or equivalent qualification;
(b)
a proven aptitude for advanced mathematical work - indicated, for example, by a high level of achievement in high school or undergraduate mathematics; and
(c)
having already passed an introductory course in statistics covering, at least, the estimation of means and proportions with confidence intervals, and the comparison of means and proportions between two groups using hypothesis tests.
(a)
a bachelor's degree in statistics, mathematics, science, psychology, medicine, pharmacy, economics, health sciences or other appropriate discipline from the University of Sydney or equivalent qualification;
(b)
a proven aptitude for advanced mathematical work - indicated, for example, by a high level of achievement in high school or undergraduate mathematics; and
(c)
having already passed an introductory course in statistics covering, at least, the estimation of means and proportions with confidence intervals, and the comparison of means and proportions between two groups using hypothesis tests.
(a)
a bachelor's degree in statistics, mathematics, science, psychology, medicine, pharmacy, economics, health sciences or other appropriate discipline from the University of Sydney or equivalent qualification;
(b)
a proven aptitude for advanced mathematical work - indicated, for example, by a high level of achievement in high school or undergraduate mathematics; and
(c)
having already passed an introductory course in statistics covering, at least, the estimation of means and proportions with confidence intervals, and the comparison of means and proportions between two groups using hypothesis tests.
6 Requirements for award
(1)
The units of study that may be taken for these awards are set out in the Table of Units of Study: Biostatistics.
(2)
To qualify for the award of the Graduate Certificate of Biostatistics a candidate must successfully complete 24 credit points, comprising:
(3)
To qualify for the award of the Graduate Diploma of Biostatistics a candidate must successfully complete 48 credit points, comprising:
(4)
To qualify for the award of the Master of Biostatistics a candidate must successfully complete 72 credit points, comprising:
(c)
a minimum of 6 and a maximum of 12 credit points of units of study from Part 3 of the Table; and
(d)
a minimum of 6 and a maximum of 12 credit points of workplace project units of study from Part 4 of the Table.
7 Transitional provisions
Table of Units of Study: Biostatistics
| Unit of study | Credit points | A: Assumed knowledge P: Prerequisites C: Corequisites N: Prohibition | Session |
|---|---|---|---|
Part 1 |
|||
| PUBH5010 Epidemiology Methods and Uses |
6 | N BSTA5011 |
Semester 1 |
| BSTA5011 Epidemiology for Biostatisticians |
6 | N PUBH5010 |
Semester 2 |
Part 2 |
|||
| Graduate diploma students, with no waivers, must complete all units of study from Part 2 of table, except BSTA5009 | |||
| BSTA5009 is a compulsory unit of study for Master's students | |||
| BSTA5001 Mathematics Background for Biostatistics |
6 | Semester 1 Semester 2 |
|
| BSTA5002 Principles of Statistical Inference |
6 | P BSTA5023 |
Semester 1 Semester 2 |
| BSTA5004 Data Management & Statistical Computing |
6 | Semester 1 Semester 2 |
|
| BSTA5006 Design of Randomised Controlled Trials |
6 | P BSTA5001 and (BSTA5011 or PUBH5010) |
Semester 2 |
| BSTA5007 Linear Models |
6 | P BSTA5002 and (BSTA5011 or PUBH5010) |
Semester 2 |
| BSTA5008 Categorical Data and GLMs |
6 | C BSTA5007 |
Semester 2 |
| BSTA5009 Survival Analysis |
6 | P BSTA5007 |
Semester 1 |
| BSTA5023 Probability and Distribution Theory |
6 | P BSTA5001 |
Semester 1 Semester 2 |
Part 3 |
|||
| BSTA5003 Health Indicators and Health Surveys |
6 | C BSTA5001 |
Semester 1 |
| BSTA5005 Clinical Biostatistics |
6 | P BSTA5001 and (BSTA5011 or PUBH5010) C BSTA5002 |
Semester 1 |
| BSTA5012 Longitudinal and Correlated Data |
6 | P BSTA5008 |
Semester 1 |
| BSTA5013 Bioinformatics |
6 | P BSTA5007 |
Semester 2 |
| BSTA5014 Bayesian Statistical Methods |
6 | P BSTA5008 |
Semester 2 |
| BSTA5015 Advanced Clinical Trials |
6 | P BSTA5006, BSTA5007 |
Semester 2 |
| PUBH5215 Introductory Analysis of Linked Data |
6 | P PUBH5018 and (PUBH5010 or BSTA5011) and (PUBH5211 or BSTA5004) |
Semester 2b |
| BSTA5014 is only available in even years. | |||
Part 4 |
|||
| BSTA5020 Workplace Project Portfolio Part A |
6 | P 24 credit points including BSTA5004 and BSTA5007 C BSTA5021 N BSTA5022 Note: Department permission required for enrolment |
Semester 1 Semester 2 |
| BSTA5021 Workplace Project Portfolio Part B |
6 | P 24 credit points including BSTA5004 and BSTA5007 Note: Department permission required for enrolment |
Semester 1 Semester 2 |
| BSTA5022 Workplace Project Portfolio Part C |
6 | P 24 credit points including BSTA5004 and BSTA5007 N BSTA5020 Note: Department permission required for enrolment |
Semester 1 Semester 2 |
| Master's degree students must submit a Workplace Project Portfolio, comprising either two projects (Part A and Part B) or one project (Part C). | |||
| A student must be enrolled in order to submit the workplace project portfolio. If a student is not able to submit his/her workplace project portfolio after enrolling once in Part C or once in both Part A and Part B, then he/she must re-enrol in a minimum of six credit points of workplace project portfolio units of study, with the concomitant financial liability, every semester until he/she submits. | |||
Further enquiries
Professor Judy Simpson
Phone: +61 2 9351 4369/5994
Fax: + 61 2 9351 5049
Email:
Website: sydney.edu.au/medicine/public-health/future/coursework/biostats
Unit of Study Descriptions for 2011
BSTA5001 Mathematics Background for Biostatistics
Credit points: 6 Teacher/Coordinator: Dr Gary Glonek, University of Adelaide (semester 1), Dr Maurizio Manuguerra, Macquarie University (semester 2) Session: Semester 1,Semester 2 Classes: 8-12 hours total study time per week, distance learning Assessment: 3xassignments (20%, 40% and 40%) Campus: Camperdown/Darlington Mode of delivery: Distance Education
The aim of this unit is to provide students with the mathematics required for studying biostatistics at a more rigorous level. On completion of this unit students should be able to follow the mathematical demonstrations and proofs used in biostatistics at Masters degree level, and to understand the mathematics behind statistical methods introduced at that level. The intention is to allow students to concentrate on statistical concepts in subsequent units, and not be distracted by the mathematics employed. Content: basic algebra and analysis; exponential functions; calculus; series, limits, approximations and expansions; matrices and numerical methods.
Textbooks
Anton H, Bivens I, Davis S. Calculus: early transcendentals combined, 9th edition. Wiley, 2009. ISBN 978-0-470-18345-8
BSTA5002 Principles of Statistical Inference
Credit points: 6 Teacher/Coordinator: Ms Rachel O'Connell and Ms Liz Barnes, University of Sydney (semester 1), Dr Patrick Kelly, University of Sydney (semester 2) Session: Semester 1,Semester 2 Classes: 8-12 hours total study time per week, distance learning Prerequisites: BSTA5023 Assessment: 2xwritten assignments (2x35%) and practical exercises (30%) Campus: Camperdown/Darlington Mode of delivery: Distance Education
The aim of this unit is to provide a strong mathematical and conceptual foundation in the methods of statistical inference, with an emphasis on practical aspects of the interpretation and communication of statistically based conclusions in health research. Content covered includes: review of the key concepts of estimation and construction of Normal-theory confidence intervals; frequentist theory of estimation including hypothesis tests; methods of inference based on likelihood theory, including use of Fisher and observed information and likelihood ratio; Wald and score tests; an introduction to the Bayesian approach to inference; an introduction to distribution-free statistical methods.
Textbooks
Notes supplied. Recommended reference books (not compulsory): Azzalini A. Statistical Inference Based on the Likelihood. Chapman and Hall, London 1996. ISBN 978-0-412-60650-2 Clayton D, Hills M. Statistical Models in Epidemiology. Oxford University Press, Oxford, 1993. ISBN 978-0-19-852221-8. Wackerley DD, Mendenhall W, Scheaffer RL. Mathematical Statistics with Applications. Duxbury Press, 2007. ISBN 978-0-495-11081-1
BSTA5003 Health Indicators and Health Surveys
Credit points: 6 Teacher/Coordinator: Professor Judy Simpson, University of Sydney Session: Semester 1 Classes: 8-12 hours total study time per week, distance learning Corequisites: BSTA5001 Assessment: 4xwritten assignments (20%, 30%, 25%, 25%) Campus: Camperdown/Darlington Mode of delivery: Distance Education
On completion of this unit students should be able to derive and compare population measures of mortality, illness, fertility and survival, be aware of the main sources of routinely collected health data and their advantages and disadvantages, and be able to collect primary data by a well-designed survey and analyse and interpret it appropriately. Content covered in this unit includes: Routinely collected health-related data; quantitative methods in demography, including standardisation and life tables; health differentials; design and analysis of population health surveys including the roles of stratification, clustering and weighting.
Textbooks
Scheaffer RL, Mendenhall W, Ott RL. Elementary Survey Sampling. 6th edition. Wadsworth 2006. ISBN 978-0-534-41805-2. Notes supplied
BSTA5004 Data Management & Statistical Computing
Credit points: 6 Teacher/Coordinator: Professor Cate D'Este and Mr Stephen Halpin, University of Newcastle (semester 1), Associate Professor Lyle Gurrin and Mr Kris Jamsen, University of Melbourne (semester 2) Session: Semester 1,Semester 2 Classes: 8-12 hours total study time per week, distance learning Assessment: 3x written assignments (30%, 35%, 35%) Campus: Camperdown/Darlington Mode of delivery: Distance Education
The aim of this unit is to introduce students to essential concepts and tools required for the management, manipulation, display and analysis of data using the Stata and SAS statistical software packages. Content includes: relational databases and how to explore them using Stata and SAS; using Stata and SAS to import, check, inspect and manipulate data, including appending, merging, using dates, transposing longitudinal data; fundamental programming skills for efficient and reproducible use of these packages, including loops, arguments and programs/macros; data display and summary presentation, including advanced graphics to produce publication-quality graphs.
Textbooks
Recommended if you have not used SAS or Stata before:
BSTA5005 Clinical Biostatistics
Credit points: 6 Teacher/Coordinator: Professor Annette Dobson, Dr Mark Jones, University of Queensland Session: Semester 1 Classes: 8-12 hours total study time per week, distance learning Prerequisites: BSTA5001 and (BSTA5011 or PUBH5010) Corequisites: BSTA5002 Assessment: 4xwritten assignments (4x23%) and online discussions (8%) Campus: Camperdown/Darlington Mode of delivery: Distance Education
The aim of this unit is to enable students to use correctly statistical methods of particular relevance to evidence-based health care and to advise clinicians on the application of these methods and interpretation of the results. This unit will look at: Clinical agreement: Bland-Altman method, kappa statistics, intraclass correlation; diagnostic tests: sensitivity, specificity, predictive value, ROC curves, likelihood ratios; statistical process control: special and common causes of variation, Shewhart CUSUM and EWMA charts; systematic reviews: process estimating treatment effect, assessing heterogeneity, publication bias.
Textbooks
Notes supplied
BSTA5006 Design of Randomised Controlled Trials
Credit points: 6 Teacher/Coordinator: Professor Philip Ryan, University of Adelaide Session: Semester 2 Classes: 8-12 hours total study time per week, distance learning Prerequisites: BSTA5001 and (BSTA5011 or PUBH5010) Assessment: 3xwritten assignments (30%, 30%, 40%) Campus: Camperdown/Darlington Mode of delivery: Distance Education
The aim of this unit is to enable students to understand and apply the principles of design and analysis of experiments, with a particular focus on randomised controlled trials (RCTs), to a level where they are able to contribute effectively as a statistician to the planning, conduct and reporting of a standard RCT. This unit covers: Principles and methods of randomisation in controlled trials; treatment allocation, blocking, stratification and allocation concealment; parallel, factorial and crossover designs including n-of-1 studies; practical issues in sample size determination; intention-to-treat principle; phase I dose-finding studies; phase II safety and efficacy studies; interim analyses and early stopping; multiple outcomes/endpoints, multiple tests and subgroup analyses, including adjustment of significance levels and P-values; reporting trial results and use of the CONSORT statement.
Textbooks
Piantadosi S. Clinical Trials: a Methodological Perspective, 2nd edition. Wiley 2005. ISBN 978-0-471-72781-1 Notes supplied
BSTA5007 Linear Models
Credit points: 6 Teacher/Coordinator: Professor John Carlin, University of Melbourne, Professor Andrew Forbes, Monash University Session: Semester 2 Classes: 8-12 hours total study time per week, distance learning Prerequisites: BSTA5002 and (BSTA5011 or PUBH5010) Assessment: 2x written assignments (35%, 40%), submitted exercises (20%), online quizzes (5%) Campus: Camperdown/Darlington Mode of delivery: Distance Education
The aim of this unit is to enable students to apply methods based on linear models to biostatistical data analysis, with proper attention to underlying assumptions and a major emphasis on the practical interpretation and communication of results. This unit will cover: the method of least squares; regression models and related statistical inference; flexible nonparametric regression; analysis of covariance to adjust for confounding; multiple regression with matrix algebra; model construction and interpretation (use of dummy variables, parametrisation, interaction and transformations); model checking and diagnostics; regression to the mean; handling of baseline values; the analysis of variance; variance components and random effects.
Textbooks
Recommended: Kutner MH, Nachtsheim CJ, Neter J, Li W. Applied Linear Statistical Models. 5th edition. McGraw-Hill/Irwin 2005. ISBN 978-0-07-310874-2 Notes supplied.
BSTA5008 Categorical Data and GLMs
Credit points: 6 Teacher/Coordinator: Professor Annette Dobson, Associate Professor Michael Coory, University of Queensland. Session: Semester 2 Classes: 8-12 hours total study time per week, distance learning Corequisites: BSTA5007 Assessment: submitted exercises (6x4%), 2x written assignments (2x35%), online discussions (6%) Campus: Camperdown/Darlington Mode of delivery: Distance Education
The aim of this unit is to enable students to use generalised linear models (GLMs) and other methods to analyse categorical data, with proper attention to underlying assumptions. There is an emphasis on the practical interpretation and communication of results to colleagues and clients who might not be statisticians. This unit covers: Introduction to and revision of conventional methods for contingency tables especially in epidemiology; odds ratios and relative risks, chi-squared tests for independence, Mantel-Haenszel methods for stratified tables, and methods for paired data. The exponential family of distributions; generalised linear models (GLMs), and parameter estimation for GLMs. Inference for GLMs - including the use of score, Wald and deviance statistics for confidence intervals and hypothesis tests, and residuals. Binary variables and logistic regression models - including methods for assessing model adequacy. Nominal and ordinal logistic regression for categorical response variables with more than two categories. Count data, Poisson regression and log-linear models.
Textbooks
Notes supplied
BSTA5009 Survival Analysis
Credit points: 6 Teacher/Coordinator: Dr Ken Beath, Macquarie University Session: Semester 1 Classes: 8-12 hours total study time per week, distance learning Prerequisites: BSTA5007 Assessment: 3x written assignments (3x22%), 1x at-home examination (26%), online participation (8%) Campus: Camperdown/Darlington Mode of delivery: Distance Education
The aim of this unit is to enable students to analyse data from studies in which individuals are followed up until a particular event occurs, e.g. death, cure, relapse, making use of follow-up data also for those who do not experience the event, with proper attention to underlying assumptions and a major emphasis on the practical interpretation and communication of results. The content covered in this unit includes: Kaplan-Meier life tables; logrank test to compare two or more groups; Cox's proportional hazards regression model; checking the proportional hazards assumption; time-dependent covariates; multiple or recurrent events; sample size calculations for survival studies.
Textbooks
Hosmer DW, Lemeshow S, May S. Applied Survival Analysis: Regression Modeling of Time to Event Data, 2nd edition. Wiley Interscience 2008. ISBN 978-0-471-75499-2 Recommended: Cleves M, Gould W, Gutierrez R, Marchenko Y. An Introduction to Survival Analysis Using Stata, 2nd edition. Stata Press 2008. ISBN 978-1-59718-041-2. Order online at www.survey-design.com.au or www.stata.com/bookstore/bios.html. Notes supplied.
BSTA5011 Epidemiology for Biostatisticians
Credit points: 6 Teacher/Coordinator: Dr Andrew Page or Dr Chris Bain or Dr Kerrianne Watt, University of Queensland Session: Semester 2 Classes: 8-12 hours total study time per week, distance learning Prohibitions: PUBH5010 Assessment: 3x written assignments (20%, 30%,50%) Campus: Camperdown/Darlington Mode of delivery: Distance Education
On completion of this unit students should be familiar with the major concepts and tools of epidemiology, the study of health in populations, and should be able to judge the quality of evidence in health-related research literature.
This unit covers: historical developments in epidemiology; sources of data on mortality and morbidity; disease rates and standardisation; prevalence and incidence; life expectancy; linking exposure and disease (eg. relative risk, attributable risk); main types of study designs - case series, ecological studies, cross-sectional surveys, case-control studies, cohort or follow-up studies, randomised controlled trials; sources of error (chance, bias, confounding); association and causality; evaluating published papers; epidemics and epidemic investigation; surveillance; prevention; screening; the role of epidemiology in health services research and policy.
This unit covers: historical developments in epidemiology; sources of data on mortality and morbidity; disease rates and standardisation; prevalence and incidence; life expectancy; linking exposure and disease (eg. relative risk, attributable risk); main types of study designs - case series, ecological studies, cross-sectional surveys, case-control studies, cohort or follow-up studies, randomised controlled trials; sources of error (chance, bias, confounding); association and causality; evaluating published papers; epidemics and epidemic investigation; surveillance; prevention; screening; the role of epidemiology in health services research and policy.
Textbooks
Notes supplied
BSTA5012 Longitudinal and Correlated Data
Credit points: 6 Teacher/Coordinator: Professor Andrew Forbes, Monash University, Professor John Carlin, University of Melbourne Session: Semester 1 Classes: 8-12 hours total study time per week, distance learning Prerequisites: BSTA5008 Assessment: practical exercises and online discussions (20%) and 2x written assignments (2x40%) Campus: Camperdown/Darlington Mode of delivery: Distance Education
This unit aims to enable students to apply appropriate methods to the analysis of data arising from longitudinal (repeated measures) epidemiological or clinical studies, and from studies with other forms of clustering (cluster sample surveys, cluster randomised trials, family studies) that will produce non-exchangeable outcomes. Content covered in this unit includes: Paired data; the effect of non-independence on comparisons within and between clusters of observations; methods for continuous outcomes; normal mixed effects (hierarchical or multilevel) models and generalised estimating equations (GEE); role and limitations of repeated measures ANOVA; methods for discrete data; GEE and generalised linear mixed models (GLMM); methods for count data.
Textbooks
Recommended: Fitzmaurice G, Laird N, Ware J. Applied Longitudinal Analysis. John Wiley and Sons, 2004. ISBN 978-0-471-21487-8.
BSTA5013 Bioinformatics
Credit points: 6 Teacher/Coordinator: Professor Graham Wood, Macquarie University Session: Semester 2 Classes: 8-12 hours total study time per week, distance learning Prerequisites: BSTA5007 Assessment: 3xwritten assignments (3x20%), at-home exam (40%) Campus: Camperdown/Darlington Mode of delivery: Distance Education
Aim: Bioinformatics addresses problems related to the storage, retrieval and analysis of information about biological structure. This unit will provide a broad-ranging study of this application of quantitative methods in biology. Topics studied will be selected from: data sources, data retrieval, quantitative methods in genome science, proteome science, population genetics, evolutionary genetics and animal and plant breeding. A suitable preparation in statistics and in biology is strongly recommended. Content: Basic notions in biology; basic principles of population genetics; Web-based tools, data sources and retrieval; analysis of single and multiple DNA or protein sequences; hidden Markov models and their applications; evolutionary models; phylogenetic trees; analysis of microarrays; functional genomics; use of R in bioinformatics applications.
Textbooks
Durbin R, Eddy S, Krogh A, Mitchison G. Biological Sequence Analysis: Probabilistic models of proteins and nucleic acids. Cambridge University Press, 1998. ISBN 978-0-521-62971-3. Notes supplied.
BSTA5014 Bayesian Statistical Methods
Credit points: 6 Teacher/Coordinator: Dr Lyle Gurrin, University of Melbourne Session: Semester 2 Classes: 8-12 hours total study time per week, distance learning Prerequisites: BSTA5008 Assessment: Assignments 60% (2x30%) and submitted exercises 40%. Campus: Camperdown/Darlington Mode of delivery: Distance Education
The aim of this unit is to achieve an understanding of the logic of Bayesian statistical inference, i.e. the use of probability models to quantify uncertainty in statistical conclusions, and acquire skills to perform practical Bayesian analysis relating to health research problems. This unit covers: simple one-parameter models with conjugate prior distributions; standard models containing two or more parameters, including specifics for the normal location-scale model; the role of non-informative prior distributions; the relationship between Bayesian methods and standard "classical" approaches to statistics, especially those based on likelihood methods; computational techniques for use in Bayesian analysis, especially the use of simulation from posterior distributions, with emphasis on the WinBUGS package as a practical tool; application of Bayesian methods for fitting hierarchical models to complex data structures.
Textbooks
Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian Data Analysis, 2nd ed. Chapman and Hall, 2003 ISBN 978-1-58488-388-3
BSTA5015 Advanced Clinical Trials
Credit points: 6 Teacher/Coordinator: Professor Val Gebski, Ms Diana Zannino, University of Sydney Session: Semester 2 Classes: 8-12 hours total study time per week, Distance learning Prerequisites: BSTA5006, BSTA5007 Assessment: 3x written assignments (25%, 25% and 10%) and 1x at-home examination (40%) Campus: Camperdown/Darlington Mode of delivery: Distance Education
This elective unit extends and enhances the concepts developed in BSTA5006 Design of Experiments and RCTs. On completion, students have the knowledge and skills required at an advanced professional level to design and analyse clinical trials, including cross-over designs and equivalence trials, and to identify and implement statistical methods for trial monitoring and reporting, with appropriate knowledge of regulatory requirements. This unit covers: methods in RCTs for determining: stopping rules for interim analyses (O'Brien-Fleming, Peto), spending functions, stochastic curtailment; statistical principles encountered in relation to aspects of regulatory guidelines (ICH, FDA, EMEA), and related to reports prepared for data safety and monitoring committees (DSMC); design and analysis of cross-over trials (period effects, interactions); equivalence and non-inferiority trials; problems of defining and using surrogate endpoints as alternatives to direct clinical outcomes.
Textbooks
Recommended: Senn S. Cross-over trials in clinical research, 2nd edition 2002, Wiley. ISBN 978-0-47149-653-3.
BSTA5020 Workplace Project Portfolio Part A
Credit points: 6 Teacher/Coordinator: Professor Judy Simpson, University of Sydney Session: Semester 1,Semester 2 Classes: Supervision by an experienced biostatistician Prerequisites: 24 credit points including BSTA5004 and BSTA5007 Corequisites: BSTA5021 Prohibitions: BSTA5022 Assessment: There is no assessment for Part A. For Part B, the portfolio will be examined by two examiners, at least one of whom will be internal to the University of Sydney. (100%) Campus: Camperdown/Darlington Mode of delivery: Normal (lecture/lab/tutorial) Day
Note: Department permission required for enrolment
The aim of this unit is to give master's students practical experience, usually in workplace settings, in the application of knowledge and skills learnt during the coursework of the master's program. Students will provide evidence of having met this goal by presenting a portfolio made up of a preface and two project reports. The projects should not all be of the same type and must involve the use of different statistical methods and concepts. At least one project should involve complex multivariable analysis of data. Students should enrol in both Workplace Project Portfolio A and Workplace Project Portfolio Part B, either in semesters 1 and 2 respectively, or both in the same semester.
Textbooks
There are no essential readings for this unit.
BSTA5021 Workplace Project Portfolio Part B
Credit points: 6 Teacher/Coordinator: Professor Judy Simpson, University of Sydney Session: Semester 1,Semester 2 Classes: Supervision by an experienced biostatistician Prerequisites: 24 credit points including BSTA5004 and BSTA5007 Assessment: There is no assessment for Part A. For Part B, the portfolio will be examined by two examiners, at least one of whom will be internal to the University of Sydney. (100%) Campus: Camperdown/Darlington Mode of delivery: Normal (lecture/lab/tutorial) Day
Note: Department permission required for enrolment
The aim of this unit is to give master's students practical experience, usually in workplace settings, in the application of knowledge and skills learnt during the coursework of the master's program. Students will provide evidence of having met this goal by presenting a portfolio made up of a preface and two project reports. The projects should not all be of the same type and must involve the use of different statistical methods and concepts. At least one project should involve complex multivariable analysis of data. Students should enrol in both Workplace Project Portfolio A and Workplace Project Portfolio Part B, either in semesters 1 and 2 respectively, or both in the same semester.
Textbooks
There are no essential readings for this unit.
BSTA5022 Workplace Project Portfolio Part C
Credit points: 6 Teacher/Coordinator: Professor Judy Simpson, University of Sydney Session: Semester 1,Semester 2 Classes: supervision by an experienced biostatistician Prerequisites: 24 credit points including BSTA5004 and BSTA5007 Prohibitions: BSTA5020 Assessment: the portfolio will be examined by two examiners, at least one of whom will be internal to the University of Sydney Campus: Camperdown/Darlington Mode of delivery: Normal (lecture/lab/tutorial) Day
Note: Department permission required for enrolment
The aim of this unit is to give master's students practical experience, usually in workplace settings, in the application of knowledge and skills learnt during the coursework of the master's program. Students will provide evidence of having met this goal by presenting a portfolio made up of a preface and one project report. The project must involve complex multivariable analysis of data.
BSTA5023 Probability and Distribution Theory
Credit points: 6 Teacher/Coordinator: Professor Andrew Forbes, Monash University (semester 1). Associate Professor Rory Wolfe, Monash University (semester 2) Session: Semester 1,Semester 2 Classes: 8-12 hours total study time per week, distance learning Prerequisites: BSTA5001 Assessment: practical exercises (20%) and 2xwritten assignments (2x40%) Campus: Camperdown/Darlington Mode of delivery: Distance Education
This unit begins with the study of probability, random variables, discrete and continuous distributions, and the use of calculus to obtain expressions for parameters of these distributions such as the mean and variance. Joint distributions for multiple random variables are introduced together with the important concepts of independence, correlation and covariance, marginal and conditional distributions. Techniques for determining distributions of transformations of random variables are discussed. The concept of the sampling distribution and standard error of an estimator of a parameter is presented, together with key properties of estimators. Large sample results concerning the properties of estimators are presented with emphasis on the central role of the Normal distribution in these results. General approaches to obtaining estimators of parameters are introduced. Numerical simulation and graphing with Stata is used throughout to demonstrate concepts.
Textbooks
Wackerly DD, Mendenhall W, Scheaffer RL. Mathematical Statistics with Applications, 7th edition, 2008, Duxbury Press, USA. ISBN 978-0-495-11081-1
PUBH5010 Epidemiology Methods and Uses
Credit points: 6 Teacher/Coordinator: Associate Professor Tim Driscoll Session: Semester 1 Classes: 1x1hr lecture and 1x2hr tutorial per week for 13 weeks - lectures and tutorials may be completed online Prohibitions: BSTA5011 Assessment: 1x4page assignment (30%) and 1x2.5hr open-book exam (70%) Campus: Camperdown/Darlington Mode of delivery: Normal (lecture/lab/tutorial) Day or On-line
This unit provides students with core skills in epidemiology, particularly the ability to critically appraise public health and clinical epidemiological research literature. This unit covers: study types; measures of frequency and association; measurement bias; confounding/effect modification; randomized trials; systematic reviews; screening and test evaluation; infectious disease outbreaks; measuring public health impact and use and interpretation of population health data. It is expected that students spend an additional 2-3 hours preparing for their tutorials.
Textbooks
Webb, PW. Bain, CJ. and Pirozzo, SL. Essential Epidemiology: An Introduction for Students and Health Professionals: Cambridge University Press 2005.
PUBH5215 Introductory Analysis of Linked Data
Credit points: 6 Teacher/Coordinator: Professor Judy Simpson Session: Semester 2b Classes: block/intensive mode 5 days 9am-5pm Prerequisites: PUBH5018 and (PUBH5010 or BSTA5011) and (PUBH5211 or BSTA5004) Assessment: Workbook exercises (30%) and 1x assignment (70%) Campus: Camperdown/Darlington Mode of delivery: Block Mode
This unit introduces the topic of linked health data analysis. It will usually run in the last full week of November. The topic is a very specialised one and will not be relevant to most MPH students. The modular structure of the unit provides students with a theoretical grounding in the classroom on each topic, followed by hands-on practical exercises in the computing lab using de-identified linked NSW data files. The computing component assumes a basic familiarity with SAS computing syntax and methods of basic statistical analysis of fixed-format data files. Contents include: an overview of the theory of data linkage methods and features of comprehensive data linkage systems, sufficient to know the sources and limitations of linked health data sets; design of linked data studies using epidemiological principles;construction of numerators and denominators used for the analysis of disease trends and health care utilisation and outcomes; assessment of the accuracy and reliability of data sources; data linkage checking and quality assurance of the study process; basic statistical analyses of linked longitudinal health data; manipulation of large linked data files; writing syntax to prepare linked data files for analysis, derive exposure and outcome variables, relate numerators and denominators and produce results from statistical procedures at an introductory to intermediate level.
Textbooks
Notes will be distributed in class.