Biostatistics units of study
All students admitted to the Biostatistics program are expected to have passed an introductory course in statistics, covering at least an understanding of P values and confidence intervals, and the comparison of means and proportions for 2 groups (ie. t-tests and chi-squares for 2x2 tables), such as PUBH5018 Introductory Biostatistics.
For a complete list of the units of study available and their pre/co-requisites, click here (PDF).
For our recommendations for your program of study, taking into account pre- and co-requisites and any exemptions you may have, click here (PDF).
Core units of study
Most of the core units of study are compulsory for the Masters and Graduate Diploma, although some are optional, depending which course you choose. If you are eligible for exemption from a unit, you may substitute elective units for some of the core units of study.
Please see the individual Masters, Graduate Diploma and Graduate Certificate coursework structures for more information on which units are compulsory for each coursework program.
|
Unit of study code |
Unit of study name |
Credit point (CP) value |
|---|---|---|
| BSTA5001* | Mathematical Background for Biostatistics |
6CP |
| BSTA5023* | Probability and Distribution Theory |
6CP |
| BSTA5002* | Principles of Statistical Inference |
6CP |
|
PUBH5010 or BSTA5011* |
|
6CP |
| BSTA5004 | Data Management & Statistical Computing |
6CP |
| BSTA5006 | Design of Randomised Controlled Trials |
6CP |
| BSTA5007 | Linear Models |
6CP |
| BSTA5008 | Categorical Data and GLMs |
6CP |
| BSTA5009** | Survival Analysis |
6CP |
| BSTA5020# | Workplace Project Portfolio, Part A |
6CP |
| BSTA5021# | Workplace Project Portfolio, Part B |
6CP |
| BSTA5022# | Workplace Project Portfolio, Part C |
6CP |
* one or more of these units is often waived, leaving room for elective units of study
** Survival Analysis is compulsory for Masters students only
# these units are available for Masters students only
Elective units of study
Although most of the core subjects are compulsory, many students will NOT need to complete either:
- BSTA5011 Epidemiology for Biostatisticians (if you have a health research background including study of epidemiology or equivalent experience), or
- one or more of BSTA5001 Mathematical Background for Biostatistics, BSTA5023 Probability and Distribution Theory, and BSTA5002 Principles of Statistical Inference (if you have a mathematical and/or statistical background).
The following table outlines the elective units of study on offer in early years of Biostatistics coursework programs. These units have fewer pre-requisites than the more advanced electives below.
|
Unit of study code |
Unit of study name |
Credit point (CP) value |
|---|---|---|
| BSTA5003 | Health Indicators and Health Surveys |
6CP |
| BSTA5005 | Clinical Biostatistics |
6CP |
| BSTA5009** | Survival Analysis |
6CP |
** Survival Analysis is compulsory for Masters students and elective for other students.
The following table outlines the more advanced elective units of study on offer in later years of Biostatistics coursework programs. Please see the individual Masters, Graduate Diploma or Graduate Certificate coursework program structures for more information on biostatistics units of study requirements.
|
Unit of study code |
Unit of study name |
Credit point (CP) value |
|---|---|---|
| BSTA5012 | Longitudinal and Correlated Data |
6CP |
| BSTA5013 | Bioinformatics |
6CP |
| BSTA5014* | Bayesian Statistical Methods |
6CP |
| BSTA5015 | Advanced Clinical Trials |
6CP |
* This unit is offered in even years only
Local students with an interest in the analysis of linked administrative health data sets using SAS may also take PUBH5215 Introductory Analysis of Linked Data and credit it towards their Biostatistics degree. This unit is run only as a 1-week face-to-face intensive course in the last full week in November each year. Pre-requisites are Epidemiology and BSTA5004 - Data Management and Statistical Computing.
For more details, see PUBH5215 Introductory Analysis of Linked Data (pdf).
Unit of study outlines
BSTA5001 - Mathematical Background for Biostatistics (MBB)
Coordinator: Dr Gary Glonek, School of Mathematical Sciences, University of Adelaide (Semester 1), Dr Maurizio Manuguerra, Department of Statistics, Macquarie University (Semester 2)
Prerequisites: None
Value: 6 credit points
Offered: Semester 1 and 2, distance learning
Aim: 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; linear algebra, matrices and determinants; numerical methods.
Assessment: Assignments (100%): functions (20%), calculus (40%), matrices and numerical methods (40%)
Prescribed texts: Anton H, Bivens I, Davis S. Calculus: early transcendentals combined. 9th edition. Wiley, 2009. ISBN 978-0-470-18345-8.
Anton, Howard. Elementary Linear Algebra. 10th edition, Wiley 2010. ISBN 978-0-470-45821-1.
Computer software: Microsoft Excel OR Stata statistical software
BSTA5023 - Probability and Distribution Theory (PDT)
Coordinator: Prof Andrew Forbes (Semester 1), A/Prof Rory Wolfe (Semester 2), Department of Epidemiology and Preventive Medicine, Monash University
Prerequisites: BSTA5001 - Mathematical Background for Biostatistics
Value: 6 credit points
Offered: Semester 1 and 2, distance learning
Aim: This unit focuses on applying the calculus-based techniques learned in BSTA5001 Mathematical Background for Biostatistics to the study of probability and statistical distributions. These two units, together with the subsequent BSTA5002 Principles of Statistical Inference unit, provide the core prerequisite mathematical statistics background required for the study of later units in the Graduate Diploma or Masters degree.
Content: 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.
Assessment: Two written assignments, each worth 40%, submission of selected practical written exercises (20%).
Prescribed texts: Wackerly DD, Mendenhall W, Scheaffer RL. Mathematical Statistics with Applications, 7th edition, 2008, Duxbury Press, USA. ISBN 978-0-495-11081-1.
Computer software: Stata statistical software
BSTA5002 - Principles of Statistical Inference (PSI)
Coordinator: Dr Rachel O’Connell and Ms Liz Barnes, NHMRC Clinical Trials Centre, University of Sydney (Semester 1), and Dr Patrick Kelly, Sydney School of Public Health, University of Sydney (Semester 2)
Prerequisites: BSTA5001 - Mathematical Background for Biostatistics,
BSTA5023 - Probability and Distribution Theory
Value: 6 credit points
Offered: Semester 1 (and Semester 2 from 2009), distance learning
Aim: 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: 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.
Assessment: Assignments 70% (2 written assignments worth 35% each), and submission of selected practical exercises 30%
Texts: Recommended reference material, not compulsory:
Azzalini A. Statistical Inference: Based on the Likelihood. Chapman and Hall, London 1996. ISBN 978-0-412-60650-2
Clayton and Hills. Statistical Models in Epidemiology. Oxford University Press, Oxford 1993. ISBN 978-0-198-52221-8
Wackerley D, Mendenhall W, Scheaffer RL. Mathematical Statistics with Applications. Duxbury Press, 2007. ISBN 978-0-495-11081-1
Computer software: SAS OR Stata statistical software
BSTA5003 - Health Indicators and Health Surveys (HIS)
Coordinator: Prof Judy Simpson, Sydney School of Public Health, University of Sydney
Pre/Co-requisites: BSTA5001 - Mathematical Background for Biostatistics
Value: 6 credit points
Offered: Semester 1, distance learning
Aim: 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: 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.
Assessment: Assignments 100% (4 written assignments: 20%, 30%, 25%, 25%)
Prescribed texts: Scheaffer RL, Mendelhall W, Ott RL. Elementary Survey Sampling. 6th edition. Wadsworth, 2006. ISBN 978-0-534-41805-2
Computer software: SAS OR Stata statistical software, and Microsoft Excel
BSTA5004 - Data Management & Statistical Computing (DMC)
Coordinator: Dr Patrick McElduff, CCEB, University of Newcastle (Semester 1), Dr Robert MacInnis, School of Population Health, University of Melbourne (Semester 2)
Prerequisites: None
Value: 6 credit points
Offered: Semester 1 and 2, distance learning
Aim: 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: 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.
Assessment: Assignments 100% (3 written assignments worth 30%, 35%, 35%)
Texts: Recommended if you have not used SAS or Stata before:
Cody R, Smith J. Applied Statistics & the SAS Programming Language. 5th edition. Prentice Hall, 2006. ISBN 978-0-13-146532-9
Hills M & De Stavola B. A Short Introduction to Stata for Biostatistics. Timberlake 2009. ISBN 978-0-9557076-1-2. Order online at:
Survey, Design & Analysis: www.survey-design.com.au
Computer software: SAS and Stata software and Microsoft Access.
BSTA5005 - Clinical Biostatistics (CLB)
Coordinator: Professor Annette Dobson, Dr Mark Jones, Professor Gita Mishra, School of Population Health, University of Queensland
Pre/Co-requisites: Epidemiology,
BSTA5001 - Mathematical Background for Biostatistics,
BSTA5023 - Probability and Distribution Theory,
BSTA5002 - Principles of Statistical Inference
Value: 6 credit points
Offered: Semester 1, distance learning
Aim: 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.
Content: Clinical agreement: kappa statistics, intraclass correlation; Bland-Altman method; 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.
Assessment: Assignments 92% (4 written assignments, each worth 23%); 8% for online discussions
Prescribed texts: Nil
Computer software: Stata statistical software
BSTA5006 - Design of Randomised Controlled Trials (DES)
Coordinator: Prof Philip Ryan, Data Management & Analysis Centre, Discipline of Public Health, University of Adelaide
Pre-requisites: Epidemiology,
BSTA5001 - Mathematical Background for Biostatistics
Value: 6 credit points
Offered: Semester 2, distance learning
Aim: 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.
Content: 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.
Assessment: Assignments 100% (3 written assignments, the first two worth 30% each and the final assignment worth 40%)
Prescribed text: Piantadosi S. Clinical Trials: A Methodological Perspective, 2nd edition. John Wiley & Sons, 2005. ISBN 978-0-471-72781-1
Computer software: Nil
BSTA5007 - Linear Models (LMR)
Coordinators: Prof Andrew Forbes, Department of Epidemiology and Preventive Medicine, Monash University and Prof John Carlin, School of Population Health, University of Melbourne
Prerequisites: Epidemiology,
BSTA5001 - Mathematical Background for Biostatistics,
BSTA5023 - Probability and Distribution Theory,
BSTA5002 - Principles of Statistical Inference
Value: 6 credit points
Offered: Semester 2, distance learning.
Aim: 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.
Content: 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, parameterisation, interaction and transformations); model checking and diagnostics; regression to the mean; handling of baseline values; the analysis of variance; variance components and random effects.
Assessment: Assignments 75% (two case study assignments worth 35% and 40%), practical exercises 20%, online quizzes 5%
Recommended texts: Kutner MH, Nachtsheim CJ, Neter J, Li W.
Applied Linear Statistical Models. 5th edition. McGraw-Hill/Irwin 2005. ISBN 978-0-073-10874-2
Computer software: Stata statistical software
BSTA5008 - Categorical Data and GLMs (CDA)
Coordinator: Professor Annette Dobson, Dr Mark Jones, Professor Gita Mishra, School of Population Health, University of Queensland
Prerequisites: Epidemiology,
BSTA5001 - Mathematical Background for Biostatistics,
BSTA5023 - Probability and Distribution Theory,
BSTA5002 - Principles of Statistical Inference
Co-requisite: BSTA5007 - Linear Models
Value: 6 credit points
Offered: Semester 2, distance learning
Aim: To enable students to use generalised linear models (GLMs) and other methods to analyse categorical data with proper attention to the underlying assumptions. There is an emphasis on the practical interpretation and communication of results to colleagues and clients who may not be statisticians.
Content: 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; generalized 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.
Assessment: Assignments 70% (2 written assignments each worth 35%), practical exercises 24% (6 x 4%), online discussions 6%
Prescribed texts: Nil
Computer software: Stata statistical software or similar
BSTA5009 - Survival Analysis (SVA)
Coordinator: Dr Ken Beath, Department of Statistics, Macquarie University
Prerequisites: Epidemiology,
BSTA5001 - Mathematical Background for Biostatistics,
BSTA5023 - Probability and Distribution Theory,
BSTA5002 - Principles of Statistical Inference,
BSTA5007 - Linear Models
Value: 6 credit points
Offered: Semester 1, distance learning
Aim: 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.
Content: 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.
Assessment: Assignments 66% (3 written assignments worth 22% each), at-home examination 26%, online participation 8%.
Prescribed text: Hosmer D W, 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 text: Cleves M, Gould W, Gutierrez R, Marchenko Y. An Introduction to Survival Analysis Using Stata. 2010 Stata Press. ISBN 978-1-59718-074-0. Order online at:
Survey, Design & Analysis: www.survey-design.com.au
Computer software: Stata statistical software
Epidemiology
PUBH5010 - Epidemiology Methods and Uses (EPI)
Coordinator: A/Prof Tim Driscoll, Sydney School of Public Health, University of Sydney
Prerequisites: None
Value: 6 credit points
Offered: Semester 1, on campus or distance learning
Aim: 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 of research literature in health-related research literature.
Content: Topics include measures of frequency and association (eg. relative risk, attributable risk); main types of study designs – 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.
Assessment: written assignment (30%) and exam (70%).
For more details, see PUBH5010 Epidemiology Methods and Uses (pdf).
NB: Students may take either PUBH5010 in Semester 1 or BSTA5011 in Semester 2
BSTA5011 - Epidemiology for Biostatisticians (EPI)
Coordinator: Dr Andrew Page or Dr Chris Bain or Dr Kerrianne Watt, School of Population Health, University of Queensland
Prerequisites: None
Value: 6 credit points
Offered: Semester 2, distance learning only
Aim: 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 of research literature in health-related research literature.
Content: Topics include measures of frequency and association (eg. relative risk, attributable risk); main types of study designs – 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.
Assessment: 3 written assignments (20%, 30%, 50%) and online exam (20%)
For more details, see PUBH7600 Introduction to Epidemiology (University of Queensland).
BSTA5020 - Workplace Project Portfolio, Part A (WPPA)*
BSTA5021 - Workplace Project Portfolio, Part B (WPPB)
BSTA5022 - Workplace Project Portfolio, Part C (WPPC)*
Coordinator: Prof Judy Simpson, Sydney School of Public Health, University of Sydney
Prerequisites: Minimum of 4 units including BSTA5004 - Data Management and Statistical Computing and BSTA5007 - Linear Models
Co-requisite: Part B is a co-requisite for Part A.
Value: 6 credit points each
Offered: Semester 1 or 2.
Students wishing to do 2 projects should enrol in both Parts A & B, either in semesters 1 & 2 respectively, or both in the same semester.
Students wishing to do 1 project should enrol in Part C.
Aim: To give Masters students practical experience, usually in workplace settings, in the application of knowledge and skills learnt during the coursework of the Masters program.
Content: Students will complete 1 or 2 projects under supervision. If a student does 2 projects, they should not both 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.
Assessment: Students should complete a portfolio, comprising a preface and one or two project reports, as described in the WPP Guidelines. The portfolio should be submitted by 30 June or 30 November and will be examined by two examiners, at least one of whom will be internal to the University of Sydney.
NOTE: These units are for Masters students only, and require supervision by a biostatistician approved by the University of Sydney. See the WPP Guidelines for a full description of the requirements for these units.
Adequate supervisory arrangements must be in place before students commence this unit. Students wishing to complete the Masters Degree should discuss options for WPP with the Biostatistics program coordinator.
*Students enrolling in Part A or Part C should complete a project proposal, as described in the WPP Guidelines.
BSTA5012 - Longitudinal and Correlated Data (LCD)
Coordinators: Prof John Carlin, School of Population Health, University of Melbourne and Prof Andrew Forbes, Department of Epidemiology and Preventive Medicine, Monash University
Prerequisites: Epidemiology,
BSTA5001 - Mathematical Background for Biostatistics,
BSTA5023 - Probability and Distribution Theory,
BSTA5002 - Principles of Statistical Inference,
BSTA5007 - Linear Models,
BSTA5008 - Categorical Data & GLMs
Value: 6 credit points
Offered: Semester 1, distance learning
Aim: 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: 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 generalized linear mixed models (GLMM); methods for count data.
Assessment: Assignments 100% (20% practical exercises, two written assignments worth 40% each)
Texts: Recommended: Fitzmaurice G, Laird N, Ware J. Applied Longitudinal Analysis. John Wiley and Sons, 2004. ISBN: 978-0-471-21487-8
Computer software: Stata and SAS statistical software
BSTA5013 - Bioinformatics (BIF)
Coordinator: Prof Graham Wood or Mr Stephen Brown, Department of Statistics, Macquarie University
Prerequisites: Epidemiology,
BSTA5001 - Mathematical Background for Biostatistics,
BSTA5023 - Probability and Distribution Theory,
BSTA5002 - Principles of Statistical Inference,
BSTA5007 - Linear Models
Value: 6 credit points
Offered: Semester 2, distance learning
Aim: Bioinformatics addresses problems related to the storage, retrieval and analysis of information about biological structure. This unit provides a broad-ranging study of this application of quantitative methods in biology.
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.
Assessment: 60% assignment (3 written assignments worth 20% each), at-home exam 40%.
Prescribed texts: 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.
Computer software: R freeware will be downloaded
BSTA5014 Bayesian Statistical Methods (BAY)
Coordinator: A/Prof Lyle Gurrin, School of Population Health, University of Melbourne
Prerequisites: Epidemiology,
BSTA5001 - Mathematical Background for Biostatistics,
BSTA5023 - Probability and Distribution Theory,
BSTA5002 - Principles of Statistical Inference,
BSTA5007 - Linear Models,
BSTA5008 - Categorical Data & GLMs
Value: 6 credit points
Offered: Semester 2 (even years only), distance learning
Aim: 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.
Content: Topics include 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 noninformative 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.
Assessment: 60% assignments (two major assignments worth 30% each), practical exercises 40%
Prescribed texts: Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian Data Analysis, 2nd ed. Chapman and Hall, 2003. ISBN 978-1-58488-388-3
Computer software: Unit coordinator will advise (no licensing costs involved)
BSTA5015 - Advanced Clinical Trials (ACT)
Coordinator: Prof Val Gebski, NHMRC Clinical Trials Centre, University of Sydney
Prerequisites: Epidemiology,
BSTA5001 - Mathematical Background for Biostatistics,
BSTA5023 - Probability and Distribution Theory,
BSTA5002 - Principles of Statistical Inference,
BSTA5006 - Design of Randomised Controlled Trials,
BSTA5007 - Linear Models
Value: 6 credit points
Offered: Semester 2, distance learning
Aim: This elective unit extends and enhances the concepts developed in BSTA5006 Design of Randomised Controlled Trials. On completion, students will have knowledge and skills at an advanced professional level of the statistical principles, issues and methods involved in the design and analysis of randomised controlled trials.
Content: Methods in RCTs for determining stopping rules for interim analyses (O'Brien-Fleming, Peto), spending functions, stochastic curtailment; analysis methods used with cross-over designs (period effects, interactions), preference outcomes, N-of-1 studies and equivalence and non-inferiority trials; statistical principles encountered in relation to aspects of regulatory/reporting guidelines (ICH, FDA, EMEA, CONSORT), and related to reports prepared for data safety and monitoring committees (DSMC). The subject will also cover statistical issues in systematic reviews and meta-analysis, including alternative approaches to combining estimates of a treatment difference across trials, meta-analysis using individual patient data, dealing with heterogeneity and selection bias.
Assessment: Assignments 60% (3 written assignments worth 25%, 25%, 10%), at-home examination 40%
Recommended texts: Senn S. Cross-over trials in clinical research, 2nd edition, 2002, Wiley. ISBN 978-0-471-49653-3
Jennison C, Turnbull BW. Group sequential methods with applications to clinical trials, 2000, Chapman & Hall. ISBN 978-0-849-30316-8
Computer software: Stata or SAS.
An ACT specific software (free ware) is supplied by the unit coordinator.