Epidemiology (EPI)

MA GD GC

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.

COORDINATORS:

A/Prof Lisa Hall University of Queensland, School of Public Health Semester 1

1 2

Mathematical Foundations for Biostatistics (MFB)

MA GD

PREREQUISITES: None

On completion of this unit students will 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.

COORDINATORS:

Prof Andrew Forbes Monash University, Department of Epidemiology and Preventive Medicine Semester 1, 2

Dr Rhys Bowden Monash University, Department of Epidemiology and Preventive Medicine Semester 1, 2

1 2

Data Management and Statistical Computing (DMC)

MA GD

PREREQUISITES: None

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.

COORDINATORS:

Dr Louise Marquart-Wilson The University of Queensland, School of Public Health Semester 2

Dr Gabriella Lincoln University of Adelaide, Adelaide Health and Medical Sciences Semester 1

1 2

Principles of Statistical Inference (PSI)

MFB MA GD

PREREQUISITES: Mathematical Foundations for Biostatistics

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.

COORDINATORS:

Ms Liz Barnes University of Sydney, NHMRC Clinical Trials Centre Semester 1

Dr Erin Cvejic University of Sydney, Sydney School of Public Health Semester 2

1 2

Regression Modelling for Biostatistics 1 (RM1)

EPI, MFB, PSI MA GD

PREREQUISITES: Epidemiology, Mathematical Foundations for Biostatistics

CO-REQUISITES: Principles of Statistical Inference

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.

COORDINATORS:

Prof Armando Teixeira-Pinto University of Sydney, Sydney School of Public Health Semester 1

A/Prof Stephane Heritier Monash University, Department of Epidemiology and Preventive Medicine Semester 2

1 2

Regression Modelling for Biostatistics 2 (RM2)

EPI, MFB, PSI, RM1 MA GD

PREREQUISITES: Epidemiology, Mathematical Foundations for Biostatistics, Principles of Statistical Inference, Regression Modelling for Biostatistics 1

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.

COORDINATORS:

Prof Gillian Heller NHMRC Clinical Trials Centre, The University of Sydney Semester 2

Dr Michael Waller University of Queensland, School of Public Health Semester 1

1

Health Indicators and Health Surveys (HIS)

MFB

CO-REQUISITES: Mathematical Foundations for Biostatistics

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.

COORDINATORS:

Dr Nicole De La Mata University of Sydney, Sydney School of Public Health Semester 1

1

Longitudinal and Correlated Data (LCD)

EPI, MFB, PSI, RM1

PREREQUISITES: Epidemiology, Mathematical Foundations for Biostatistics, Principles of Statistical Inference, Regression Modeling for Biostatistics 1

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.

COORDINATORS:

Prof Lyle Gurrin University of Melbourne, School of Population and Global Health Semester 1

1

Clinical Biostatistics (CLB)

EPI, MFB, PSI, DES, RM1

PREREQUISITES: Epidemiology, Mathematical Foundations for Biostatistics, Principles of Statistical Inference

CO-REQUISITES: Design of Randomised Controlled Trials, Regression Modelling for Biostatistics 1

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.

COORDINATORS:

Dr Louise Marquart-Wilson The University of Queensland, School of Public Health Semester 1

2

Design of Randomised Controlled Trials (DES)

EPI, MFB

PREREQUISITES: Epidemiology, Mathematical Foundations for Biostatistics

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.

COORDINATORS:

A/Prof Lynne Giles University of Adelaide, School of Public Health Semester 2

2

Causal Inference (CSI)

EPI, MFB, RM1

PREREQUISITES: Epidemiology, Mathematical Foundations for Biostatistics, Regression Modeling for Biostatistics 1 or a multivariable regression unit of study from a Master of Public Health course or equivalent

This unit covers modern statistical methods for assessing the causal effect of a treatment or exposure from randomised or observational studies.

COORDINATORS:

A/Prof Jessica Kasza Monash University, Department of Epidemiology and Preventive Medicine Semester 2

Prof Andrew Forbes Monash University, Department of Epidemiology and Preventive Medicine Semester 2

2

Machine Learning for Biostatistics (MLB)

EPI, MFB, PSI, RM1

PREREQUISITES: Epidemiology, Mathematical Foundations for Biostatistics, Principles of Statistical Inference, Regression Modelling for Biostatistics 1

Recent years have brought a rapid growth in the amount and complexity of health data captured, requiring 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 modern machine learning methods particularly useful for large and complex health data.

COORDINATORS:

Prof Armando Teixeira-Pinto University of Sydney, Sydney School of Public Health Semester 2

Dr Andrew Grant University of Sydney, Sydney School of Public Health Semester 2

2

Bayesian Statistical Methods (BAY)

EPI, MFB, PSI, RM1

PREREQUISITES: Epidemiology, Mathematical Foundations for Biostatistics, Principles of Statistical Inference, Regression Modeling for Biostatistics 1

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.

COORDINATORS:

Dr Koen Simons University of Melbourne, School of Population and Global Health Semester 2

2

Statistical Genomics (SGX)

MFB, DMC, PSI, RM1

PREREQUISITES: Mathematical Foundations for Biostatistics, Data Management and Statistical Computing, Principles of Statistical Inference, Regression Modeling for Biostatistics 1

To learn about relevant biology and terminology, to understand the most important mathematical models and inference methods in statistical genetics, to be able to test for association between genetic variants and outcomes of interest, and to use genome-wide statistical models to help understand the genetic mechanisms underlying a trait and to predict outcomes.

COORDINATORS:

A/Prof Agus Salim University of Melbourne, School of Population and Global Health and School of Mathematics and Statistics Semester 2

Biostatistical Research/Practical Project (WPP)

RM1, DMC MA

PREREQUISITES: Minimum of 4 units, including Regression Modelling for Biostatistics 1 and Data Management & Statistical Computing

The aim of this unit is that the student gains practical experience, usually in workplace settings, in the application of knowledge and skills learnt during the coursework of the masters program.

Curriculum

Curriculum

Units

The BCA Program has 3 course options and is comprised of 15 units or individual subjects.

Epidemiology (EPI)

Mathematical Foundations for Biostatistics (MFB)

Data Management and Statistical Computing (DMC)

Principles of Statistical Inference (PSI)

Regression Modelling for Biostatistics 1 (RM1)

Regression Modelling for Biostatistics 2 (RM2)

Health Indicators and Health Surveys (HIS)

Longitudinal and Correlated Data (LCD)

Clinical Biostatistics (CLB)

Design of Randomised Controlled Trials (DES)

Causal Inference (CSI)

Machine Learning for Biostatistics (MLB)

Bayesian Statistical Methods (BAY)

Statistical Genomics (SGX)

Biostatistical Research/Practical Project (WPP)

The BCA acknowledges we live and work on the ancestral lands of Aboriginal and Torres Strait Islander peoples, who have for thousands of generations exchanged knowledge for the benefit of all. We pay our respects to those who have cared and continue to care for Country.

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