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*

 

Epidemiology

 

 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

+ This unit is only offered in odd numbered years
* This unit is only offered in even numbered years

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 late June and late 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 - Mathematics Background for Biostatistics (MBB)

Coordinator: A/Prof 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: 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; linear algebra, matrices and determinants; numerical methods.

Assessment: Three assignments (20% and 2x40%)

Prescribed texts: Anton H, Bivens I, Davis S. Calculus: early transcendentals combined. 10th edition. Wiley, 2012. ISBN 978-0-470-64769-1.
Anton, Howard. Elementary Linear Algebra. 10th edition, Wiley 2010. ISBN 978-0-470-45821-1.

Recommended reference book (not compulsory): Healy, MJR. Matrices for Statistics, 2nd edition. Oxford University Press, 2000, ISBN 987-0-470-45821-1.

Computer software: Microsoft Excel OR Stata statistical software


BSTA5023 - Probability and Distribution Theory (PDT)

Coordinator: Professor Andrew Forbes or Professor Rory Wolfe, 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: Practical exercises (30%) and two written assignments (2x35%).

Prescribed texts: Wackerly DO, Mendenhall W, Scheaffer RL. Mathematical Statistics with Applications, 7th edition, 2008, Brooks/Cole, Cengage Learning, USA. ISBN 978-0-495-11081-1.

Computer software: Stata statistical software


BSTA5002 - Principles of Statistical Inference (PSI)

Coordinator: Ms Liz Barnes and Ms Lucy Davies, 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 2, 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: Two written assignments (2x35%)) and practical exercises (30%).

Recommended texts (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
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: Four written assignments (20%, 30%, 25%, 25%).

Prescribed texts: Scheaffer RL, Mendelhall W, Ott RL, Gerow KG. Elementary Survey Sampling. 7th edition. Brooks/Cole, Cengage Learning 2011. ISBN 978-0-840-05361-9
Notes supplied

Computer software: SAS OR Stata statistical software, and Microsoft Excel


BSTA5004 - Data Management & Statistical Computing (DMC)

Coordinator: A/Prof Patrick McElduff, University of Newcastle (Semester 1), Dr Helena Romaniuk, 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 provide students with the knowledge and skills required to undertake moderate to high level data manipulation and management in preparation for statistical analysis of data typically arising in health and medical research. Students will: gain experience in data manipulation and management using two major statistical software packages (Stata and SAS); learn how to check and clean data, display and summarise data using statistical software, and link files through use of unique and non-unique identifiers; acquire fundamental programming skills for efficient use of software packages; and learn key principles of confidentiality and privacy in data storage, management and analysis.

Content: The topics covered are: Module 1 - Stata and SAS: The basics (importing and exporting data, recording data, formatting data, labelling variable names and data values; using dates, data display and summary presentation); Module 2 - Stata and SAS: graphs, data management and statistical quality assurance methods (including advanced graphics to produce publication-quality graphs); Module 3 - Data management using Stata and SAS (using functions to generate new variables, appending, merging, transposing longitudal data; programming skills for efficient and reproducible use of these packages, including lops, arguments and programs/macros).

Assessment: Three written assignments (30%, 2x35%)

Recommended texts 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 BL. A Short Introduction to Stata for Biostatistics (Updated to Stata12). Timberlake 2012. ISBN 978-0-9571708-0-3. Order online at:
www.survey-design.com.au or http://www.stata.com/bookstore/biostatistics-epidemiology
Notes supplied

Computer software: SAS and Stata software and Microsoft Access.


BSTA5005 - Clinical Biostatistics (CLB)

Coordinator: Professor Annette Dobson and Dr Mark Jones, School of Population Health, University of Queensland
Pre-requisites: BSTA5001 - Mathematical Background for Biostatistics
Epidemiology or BSTA5011 - Epidemiology for Biostatisticians,
BSTA5023 - Probability and Distribution Theory
Co-requisites: 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: 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, quality control charts; systematic reviews: estimating treatment effect, assessing heterogeneity, publication bias.

Assessment: 4 written assignments, each 25%

Prescribed texts: Notes supplied

Computer software: Stata statistical software


BSTA5006 - Design of Randomised Controlled Trials (DES)

Coordinator: Dr Lisa Yelland, Data Management & Analysis Centre, Discipline of Public Health, University of Adelaide
Pre-requisites: BSTA5001 - Mathematical Background for Biostatistics
Epidemiology or BSTA5011 - Epidemiology for Biostatisticians,
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: Three written assignments (2x30% and 40%)

Prescribed text: Piantadosi S. Clinical Trials: A Methodological Perspective, 2nd edition. John Wiley & Sons, 2005. ISBN 978-0-471-72781-1
Notes supplied

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: BSTA5001 - Mathematical Background for Biostatistics
BSTA5002 - Principles of Statistical Inference
Epidemiology or BSTA5011 - Epidemiology for Biostatisticians,
BSTA5023 - Probability and Distribution Theory
Value: 6 credit points
Offered: Semester 1 and Semester 2, distance learning.

Aim: 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.

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: Two written assignments (35% and 40%), submitted 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
Notes supplied

Computer software: Stata statistical software


BSTA5008 - Categorical Data and GLMs (CDA)

Coordinator: Professor Annette Dobson, Dr Mark Jones, School of Population Health, University of Queensland
Co-requisite: BSTA5007 - Linear Models
Pre-requisites: BSTA5001 - Mathematical Background for Biostatistics
BSTA5002 - Principles of Statistical Inference
Epidemiology or BSTA5011 - Epidemiology for Biostatisticians
BSTA5023 - Probability and Distribution Theory
Value: 6 credit points
Offered: Semester 2, distance learning

Aim: 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 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: Three written assignments (35%, 35%, 30%)

Prescribed texts: Notes supplied

Computer software: Stata statistical software or similar


BSTA5009 - Survival Analysis (SVA)

Coordinator: Dr Ken Beath, Department of Statistics, Macquarie University
Prerequisites: BSTA5001 - Mathematical Background for Biostatistics
BSTA5002 - Principles of Statistical Inference
BSTA5007 - Linear Models
Epidemiology or BSTA5011 - Epidemiology for Biostatisticians
BSTA5023 - Probability and Distribution Theory
Value: 6 credit points
Offered: Semester 1, distance learning

Aim: 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.

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: Three written assignments (2x27% and 31%), short-answer exercises (3x5%).

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
Notes supplied

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
Prohibitions: BSTA5011 - Epidemiology for Biostatisticians
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 Jolieke van der Pols, School of Population Health, University of Queensland
Prohibitions: Epidemiology
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: This unit covers: historical developments in epidemiology; sources of data on mortality and morbidity; disease rates and standardisation; prevelance and incidence; life expectancy; linking exposure and disease (ed. 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.

Assessment: 3 written assignments (20% and 2x40%)

Prescribed texts: Notes supplied

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: Dr Patrick Kelly, 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 only.

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 .

*Students enrolling in Part A or Part C should complete a project proposal, as described in the
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: BSTA5001 - Mathematical Background for Biostatistics
BSTA5002 - Principles of Statistical Inference
BSTA5007 - Linear Models
BSTA5008 - Categorical Data & GLMs
Epidemiology or BSTA5011 - Epidemiology for Biostatisticians
BSTA5023 - Probability and Distribution Theory

Value: 6 credit points
Offered: Semester 1, distance learning

Aim: The aim of this unit is 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: Two major written assignments (2x30%), four shorter written assignments (4x10%)

Recommended texts: Fitzmaurice G, Laird N, Ware J. Applied Longitudinal Analysis. John Wiley and Sons, 2004. ISBN: 978-0-471-21487-8
Notes supplied

Computer software: Stata and SAS statistical software


BSTA5013 - Bioinformatics (BIF)

Coordinator: Dr Nicola Armstrong, University of Sydney, Dr Natalie Thorne, Walter & Eliza Hall Institute
Prerequisites: BSTA5001 - Mathematical Background for Biostatistics
BSTA5002 - Principles of Statistical Inference
BSTA5007 - Linear Models
Epidemiology or BSTA5011 - Epidemiology for Biostatisticians
BSTA5023 - Probability and Distribution Theory
Value: 6 credit points
Offered: Semester 2 (odd years only), 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: Three written assignments (3x20%), 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.
Notes supplied

Computer software: R freeware will be downloaded


BSTA5014 Bayesian Statistical Methods (BAY)
(This unit is not available in 2015)

Coordinator: A/Prof Lyle Gurrin, School of Population Health, University of Melbourne
Prerequisites: BSTA5001 - Mathematical Background for Biostatistics
BSTA5002 - Principles of Statistical Inference
BSTA5007 - Linear Models
BSTA5008 - Categorical Data & GLMs
Epidemiology or BSTA5011 - Epidemiology for Biostatisticians
BSTA5023 - Probability and Distribution Theory
Value: 6 credit points
Offered: Semester 2 (even years only), distance learning

Aim: 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.

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: Two assignments (2x30%), submitted 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