Biostatistics

 

Biostatistics

Master of Biostatistics

Students must complete 72 credit points, including:
(a) 6 credit points form Part 1; and
(b) 48 credits points from Part 2; and
(c) a minimum of 6 credit points of units of study from Part 3; and
(d) a minimum of 6 and a maximum of 12 credit points of workplace project units of study from Part 4

Graduate Diploma of Biostatistics

Students must complete 48 credit points, including:
(a) 6 credit points of units of study from Part 1; and
(b) 42 credit points of units of study from Part 2.

Graduate Certificate of Biostatistics

Students must complete 24 credit points, including:
(a) 6 credit points of units of study from Part 1; and
(b) 18 credit points of units of study from Part 2 or Part 3.

Part 1

All students must complete 6 credit points from Part 1 of the table.
PUBH5010 Epidemiology Methods and Uses

Credit points: 6 Teacher/Coordinator: Professor Tim Driscoll, Dr Erin Mathieu Session: Semester 1 Classes: 1x 1hr lecture and 1x 2hr tutorial per week for 13 weeks - face to face or their equivalent online Prohibitions: BSTA5011 or CEPI5100 Assessment: 1x 6 page assignment (25%), 10 weekly quizzes (5% in total) and 1x 2.5hr supervised open-book exam (70%). For distance students, it may be possible to complete the exam externally with the approval of the course coordinator. Mode of delivery: Normal (lecture/lab/tutorial) day, Normal (lecture/lab/tutorial) evening, Online
This unit provides students with core skills in epidemiology, particularly the ability to critically appraise public health and clinical epidemiological research literature regarding public health and clinical issues. 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. In addition to formal classes or their on-line equivalent, it is expected that students spend an additional 2-3 hours at least each week preparing for their tutorials.
Textbooks
Webb, PW. Bain, CJ. and Page, A. Essential Epidemiology: An Introduction for Students and Health Professionals Third Edition: Cambridge University Press 2017.
BSTA5011 Epidemiology for Biostatisticians

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly Session: Semester 2 Classes: 8-12 hours total study time per week Prohibitions: PUBH5010 or CEPI5100 Assessment: 3 x written assignments (25%, 50%, 25%) Mode of delivery: Online
Note: Department permission required for enrolment
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.
Textbooks
Bain C, Webb P. Essential Epidemiology: An Introduction for Students and Health Professionals, 2nd edition. Cambridge University Press, 2011.

Part 2

Graduate diploma students, with no waivers, must complete all units of study from Part 2 of table, except BSTA5009.
Masters students must complete all units of study from Part 2 of the table.
BSTA5001 Mathematics Background for Biostatistics

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly Session: Semester 1,Semester 2 Classes: 8-15 hours total study time per week, depending on the amount of revision required Assessment: Assignments 100%: functions and limits (20%) calculus (40%) linear algebra (40%) Mode of delivery: Online
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; and numerical methods.
Textbooks
BSTA5001 - Compulsory: 1) Anton H, Bivens I, Davis S. Calculus: Early Transcendentals, 11th edition. Wiley, 2016. ISBN 9781118884126. 2) Anton, Howard. Elementary Linear Algebra. 11th edition, Wiley 2014. Note: There are a number of Anton versions; be sure you have the correct one. Useful but not essential text: Healy, MJR. Matrices for Statistics, 2nd edition. Oxford University Press, 2000, ISBN 978-0-470-45821-1. Notes supplied..
BSTA5002 Principles of Statistical Inference

Credit points: 6 Teacher/Coordinator: Ms Liz Barnes (Semester 1); Dr Erin Cvejic (Semester 2) Session: Semester 1,Semester 2 Classes: 8-12 hours total study time per week Prerequisites: BSTA5023 Assessment: Two major assignments worth 40% each and module exercises worth a total of 20%. Mode of delivery: Online
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
Marschner IC. Inference Principles for Biostatisticians. Chapman and Hall / CRC Pr, 2014. ISBN 978-1-48222-223-4. Notes supplied.
BSTA5004 Data Management and Statistical Computing

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly Session: Semester 1,Semester 2 Classes: 8-12 hours total study time per week Assessment: Three written assignments worth 30%, 35% and 35%. Mode of delivery: Online
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. Specific objectives are for students to: gain experience in data manipulation and management using two major statistical software packages (Stata and R); learn how to display and summarise data using statistical software; become familiar with the checking and cleaning of data; learn how to 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. The topics covered are: Module 1 - Stata and R: The basics (importing and exporting data, recoding data, formatting data, labelling variable names and data values; using dates, data display and summary presentation); and creating programs. Module 2 - Stata and R: graphs, data management and statistical quality assurance methods (including advanced graphics to produce publication-quality graphs); Module 3 - Data management using Stata and R (using functions to generate new variables, appending, merging, transposing longitudinal data; programming skills for efficient and reproducible use of these packages, including loops and arguments.
Textbooks
If you have not used R or Stata previously, it is recommended that you have access to the text for the relevant software.
BSTA5006 Design of Randomised Controlled Trials

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly Session: Semester 2 Classes: 8-12 hours total study time per week Prerequisites: BSTA5001 and (BSTA5011 or PUBH5010) Assessment: Assignments 100% (three written assignments, the first two worth 30% each and the final assignment worth 40%) Mode of delivery: Online
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: ethical considerations; 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, including surrogate outcomes, multiple tests and subgroup analyses, including adjustment of significance levels and P-values; missing data; reporting trial results and use of the CONSORT statement.
Textbooks
Matthews JNS. Introduction to Randomised Controlled Clinical Trials, 2nd edition. Chapman and Hall/CRC Press 2006. ISBN P/back: 978154886242, eBook: 9781420011302
BSTA5007 Linear Models

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly (Semester 1), Dr Timothy Schlub (Semester 2) Session: Semester 1,Semester 2 Classes: 8-12 hours total study time per week Prerequisites: BSTA5023 and (BSTA5011 or PUBH5010) Corequisites: BSTA5002 Assessment: Two major assignments worth 30% and 40% and two shorter assignments worth 10% and 20%. Mode of delivery: Online
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.
NOTE: Linear Models is an important foundation unit. Students who do not develop a strong grasp of this material will struggle to become successful biostatisticians.
Textbooks
No compulsory textbook
BSTA5008 Categorical Data and Generalised Linear Model

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly Session: Semester 2 Classes: 8-12 hours total study time per week Corequisites: BSTA5007 Assessment: 3 assignments, the first for modules 1-3 (35%), the second for modules 4-5 (35%), and the last for module 6 (30%) Mode of delivery: Online
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
References will be listed in the unit Study Guide
BSTA5009 Survival Analysis

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly Session: Semester 1 Classes: 8-12 hours total study time per week Prerequisites: BSTA5007 Assessment: 3 x assignments. Assignment 1 (30%) Censoring and Truncation, Survival Summaries, Kaplan-Meier, Simple Cox models. Assignment 2 (40%) Cox Models including interactions and stratification, Model building, diagnostics, predicted survival and cumulative hazard. Assignment 3 (30%) Time-dependent covariates, parametric models, multivariate survival, graphical presentation Mode of delivery: Online
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
Compulsory: 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 - not compulsory: Cleves M, Gould W, Gutierrez R, Marchenko Y. An Introduction to Survival Analysis Using Stata, 3rd edition. Stata Press 2010. ISBN 978-1-59718-074-0.
BSTA5023 Probability and Distribution Theory

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly Session: Semester 1,Semester 2 Classes: 8-12 hours total study time per week Prerequisites: BSTA5001 Assessment: Two written assignments (each worth 35%) and submission of selected practical written exercises from 5 modules 30%. Mode of delivery: Online
This unit will focus on applying the calculus-based techniques learned in Mathematical Background for Biostatistics (MBB) to the study of probability and statistical distributions. These two units, together with the subsequent Principles of Statistical Inference (PSI) unit, will 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.
Textbooks
Wackerly DD, Mendenhall W, Scheaffer RL. Mathematical Statistics with Applications, 7th edition, 2007, Wadsworth Publishing (ex Duxbury Press, USA)

Part 3

Masters students must complete a minimum of 6 credit points from Part 3 of the Table.
BSTA5013 is only available in odd years and BSTA5014 is only available in even years.
BSTA5003 Health Indicators and Health Surveys

Credit points: 6 Teacher/Coordinator: Associate Professor Kevin MacGeechan Session: Semester 1 Classes: 8-12 hours total study time per week Corequisites: BSTA5001 Assessment: Assignments 100% (4 written assignments worth 25% each) Mode of delivery: Online
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
Paul S. Levy, Stanley Lemeshow, Sampling of Populations: Methods and Applications, 4th edition, Wiley Interscience 2008.
BSTA5005 Clinical Biostatistics

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly Session: Semester 1 Classes: 8-12 hours total study time per week Prerequisites: BSTA5002 and BSTA5006 Corequisites: BSTA5007 Assessment: 3 written assignments (40%, 30%, 30%) Mode of delivery: Online
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. Content: Clinical trials (equivalence trials, cross-over trials); Clinical agreement (Bland-Altman methods, kappa statistics, intraclass correlation); Statistical process control (special and common causes of variation; quality control charts); Diagnostic tests (sensitivity, specificity, ROC curves); Meta-analysis (systematic reviews, assessing heterogeneity, publication bias, estimating effects from randomised controlled trials, diagnostic tests and observational studies).
Textbooks
References will be listed in the unit Study Guide
BSTA5012 Longitudinal and Correlated Data

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly Session: Semester 1 Classes: 8-12 hours total study time per week Prerequisites: BSTA5008 Assessment: Assignments 100% (two major written assignments worth 30% each (8 hours) and 5 shorter assignments each worth 8% Mode of delivery: Online
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, 2011. ISBN 978-0-471-21487-8.
BSTA5013 Bioinformatics and Statistical Genomics

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly, University of Sydney Session: Semester 2 Classes: 8-12 hours total study time per week Assessment: assignments 60% (three written assignments (each worth 20%); final at-home examination (40%) Mode of delivery: Online
Note: This unit of study is only offered in odd numbered years. It is available in 2019 Special Computer Requirements: "R" (freeware - coordinator will give instructions on how to download)
The aim of this unit is to provide students with an introduction to the field of Bioinformatics. Bioinformatics is a multidisciplinary field that combines biology with quantitative methods to help understand biological processes, such as disease progression. Content: biology basics; statistical genetics; web-based tools, data sources and data retrieval; analysis of single and multiple DNA or protein sequences; hidden Markov Models and their applications; evolutionary models; phylogenetic trees; transcriptomics (gene expression microarrays and RNA-seq); 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: Associate Professor Patrick Kelly Session: Semester 2 Classes: 8-12 hours total study time per week Assessment: Assignments 60% (2 x 30%) and submitted exercises (40%) Mode of delivery: Online
Note: Note: this unit is only offered in even numbered years. It is available in 2020.
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, Dunson DB, Vehtari A. Bayesian Data Analysis, 3rd edition. Chapman and Hall, 2003. ISBN 978-1-58488-388-3; Notes provided.
BSTA5017 Causal Inference

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly Session: Semester 2 Classes: the expected workload for this unit is 8-12 hours per week on average for 13 wks, consisting of guided readings, discussion posts, independent study and completion of assessment tasks. Prerequisites: (PUBH5010 or BSTA5011) and BSTA5023 and (BSTA5007 or PUBH5017) Assessment: two major assignments worth 30% each. these consist of statistical analysis of data from a cohort study and the preparing a report presenting the results in tables and figures with a written description of the research question, methods, results and discussion/conclusion equivalent to 1, 500 word essay. five online quizzes each worth 8% (for a total of 40%) are each equivalent to a series of short answers of about 400 words, so 2, 000 words in total. Mode of delivery: Online
This unit covers modern statistical methods that are now available for assessing the causal effect of a treatment or exposure from a randomised or observational study. The unit begins by explaining the fundamental concept of counterfactual or potential outcomes and introduces causal diagrams, known commonly as directed acyclic graphs (DAGs). DAGs enable us to visualise causal pathways to identify confounding, selection and other biases that prevent unbiased estimation of causal effects. Throughout the unit students are introduced to statistical methods for analysing data from observational studies that generate estimates with a causal interpretation. You will learn propensity score methods and how to assess whether the effects of an exposure on an outcome is mediated by one or more intermediate variables, suggesting potential mechanisms and pathways. Comparisons will be made with 'conventional' statistical methods. Emphasis will be placed on interpretation of results and understanding the assumptions required to allow inferences to be called 'causal'. Stata and R software will be used to real datasets from cohort studies.
Textbooks
There is no single prescribed text for the subject, but a number of reference books are suggested as background material.
BSTA5018 Machine Learning in Biostatistics

Credit points: 6 Teacher/Coordinator: Associate Professor Armando Teixeira-Pinto Session: Semester 2 Classes: the expected workload for this unit is 8-12 hours per week on average for 13 weeks, consisting of guided readings, discussion posts, independent study and completion of assessment tasks. Prerequisites: BSTA5007 or PUBH5217 Assessment: two major assignments worth 40% each (equivalent to 2 x 2000 words) and two short assignments worth 10% each. Mode of delivery: Online
Note: If you have completed BSTA5007 you must take BSTA5018 and BSTA5008 at the same time.
Recent years have brought a rapid growth in the amount and complexity of data in biostatistical applications. Among others, data collected in imaging, genomic, health registries, wearables, call for new statistical techniques in both predictive and descriptive learning. Machine learning algorithms for classification and prediction, complement classical statistical tools in the analysis of these data. This unit will cover several modern methods particularly useful for big and complex data. Topics include classification trees, random forests, model selection, lasso, bootstrapping, cross-validation, generalised additive model, splines, among others. The statistical software R will be used throughout the unit.
Textbooks
James G, Witten D, Hastie T, Tibshirani R, An Introduction to Statistical Learning with Applications, in R. Springer, 2003. ISBN 978-1-4614-7138-7
PUBH5215 Analysis of Linked Health Data

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly Session: Intensive June,Intensive November Classes: Block/intensive mode - 5 days, 9am - 5pm Corequisites: (PUBH5010 or BSTA5011 or CEPI5100) and (PUBH5211 or PUBH5217 or BSTA5004) Assumed knowledge: Basic familiarity with SAS computing syntax and methods of basic statistical analysis of fixed-format data files Assessment: Reflective journal (30%) and 1x data analysis assignment (70%) Mode of delivery: Block mode
Note: Department permission required for enrolment
Note: Familiarity with writing a basic SAS program. For data privacy and security reasons, the major assignment can only be completed on the computers in the Sydney School of Public Health Computer Lab. This computer lab is available 24/7 for students enrolled in this unit.
This unit introduces the topic of analysing linked health data. The topic is very specialised and is relevant to those who are familiar with writing a basic SAS program, who wish to further develop their knowledge and skills in managing and analysing linked health data, eg. hospital admissions, cancer registry, births and deaths.
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.
The unit is delivered as a workshop over 5 consecutive days. Lectures are delivered in the morning sessions and the afternoon sessions are computer labs where students gain hands-on experience using large health datasets.
The unit is usually offered twice a year, once in mid-June and once in mid-November.
Textbooks
Notes will be distributed in class.
PUBH5312 Health Economic Evaluation

Credit points: 6 Teacher/Coordinator: A/Prof Alison Hayes Session: Intensive September Classes: on-line components and 4 non-consecutive workshop days Prerequisites: HPOL5000 and (PUBH5010 or CEPI5100) and PUBH5018 Prohibitions: PUBH5302 Assessment: on-line quiz (5%), in-class presentation (5%), short answer questions, calculations, and critical appraisal (equivalent to 3000 words) (20%), critical appraisal (equivalent to 2000 words) (20%), protocol report (2000 words) (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
The overall aim of the course is to develop students' knowledge and skills of economic evaluation as an aid to priority setting in health care. Students will be introduced to the principles of economic evaluation and develop skills in the application of those principles to resource allocation choices. Emphasis will be placed on learning by case study analysis and problem solving in small groups. This unit covers: principles and different types of economic evaluation; critical appraisal guidelines; measuring and valuing benefits; methods of costing; modeling in economic evaluation, the role of the PBAC, introduction to advanced methods including use of patient-level data and data linkage. The workshops consist of interactive lectures, class exercises and quizzes.
Textbooks
Recommended book: Michael F. Drummond , Mark J. Sculpher , George W. Torrance, Bernie J. O'Brien, Greg L. Stoddart. Methods for the Economic Evaluation of Health Care Programmes (Paperback), Oxford University Press, 2005. Essential chapters available on-line.

Part 4

Masters students must complete a minimum of 6 and a maximum of 12 credit points from Part 4 of the Table. Students wishing to do one project (6cp) should enrol in BSTA5020; those wishing to do two projects (12cp) should enrol in BSTA5020 and BSTA5021.
BSTA5020 Biostatistics Research Project Part A

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly Session: Semester 1,Semester 2 Classes: Supervision by an experienced biostatistician Prerequisites: 24 credit points including BSTA5004 and BSTA5007 Assessment: Students will be assessed by writing a portfolio which includes a reflection of their learning and a report on the work conducted for the project. The portfolio will be examined by two examiners, at least one of whom will be internal to the University of Sydney. Mode of delivery: Supervision
Note: Department permission required for enrolment
The aim of the unit is to give students practical experience in the application of the knowledge and skills learnt during the coursework program. Projects can be created or provided in your workplace or by a researcher, research group or institution. The project should involve analysing real data to answer one or more research questions. The analyses must involve multivariable regression modelling.
Textbooks
There are no essential readings for this unit.
BSTA5021 Biostatistics Research Project Part B

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly Session: Semester 1,Semester 2 Classes: Supervision by an experienced biostatistician Prerequisites: 24 credit points including BSTA5004 and BSTA5007 Corequisites: BSTA5020 Assessment: Students will be assessed by writing a portfolio which includes a reflection of their learning and a report on the work conducted for the project. The portfolio will be examined by two examiners, at least one of whom will be internal to the University of Sydney. Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
This unit is for students who wish to do a second research project. This project must differ from the first project (BSTA5020) in terms of aims and statistical methods. The second project does not need to include multivariable regression modelling and could include, for example, conducting a simulation study, developing a new statistical method, designing a study or data management of large complex datasets. The aim of the unit is to give students practical experience in the application of the knowledge and skills learnt during the coursework program. Projects can be created or provided by the student's workplace or by a researcher, research group or institution.
Textbooks
There are no essential readings for this unit.