Master of Biostatistics

This subject is a core subject within the Master of Public Health, the Master of Epidemiology, the Master of Science (Epidemiology) and the Master of Biostatistics. Students should enrol in this subject early in their program of study.

Epidemiology is the study of the distribution and determinants of disease frequency in human populations and the application of this study to control health problems. It is a fundamental science of public health.

Three main tasks of epidemiology include description, causal inference and prediction. This subject focuses on the first two and emphasises the application of epidemiological evidence to informing public health practice and policy.

Description: the epidemiological measures of disease frequency and summary measures of population health are introduced and used to describe patterns and trends in disease occurrence within and between populations. The role of routinely collected data, particularly for surveillance of infectious diseases, is discussed.

Causal inference: is key to applying epidemiological evidence to controlling health problems if interventions are to be effective. In this subject, causal inference is considered within the modern counterfactual framework. Causal diagrams, which are an integral part of this approach to causal inference are introduced. The common experimental and observational study designs, and systematic reviews, and their relative strengths and weaknesses are discussed. The implications of common types of bias (selection bias, information bias, and confounding) are discussed, as are methods to minimise them. Methods to control for confounding, including standardisation, are discussed.

Differences in characteristics of the major sources of morbidity (infectious disease, non-communicable disease, and injury) are discussed in the context of prevention and early detection of disease. Transmission dynamics of infectious diseases are introduced in this context. The applicability of epidemiological evidence (external validity) to interventions in target populations is introduced. Measures of the validity and performance of tests for early detection are introduced.

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The aim of this subject is to equip students with the knowledge and skills required for moderate to high level data manipulation and management in preparation for statistical analysis of data typically encountered in health and medical research. Students will gain hands-on experience with two major statistical software packages (Stata and R), learning to efficiently manage and manipulate data, display and summarize data, check and clean datasets, and link files using unique and non-unique identifiers. Additionally, students will acquire fundamental graphing and programming skills for each of these software packages and will be introduced to key principles of confidentiality and privacy in data storage, management, and analysis.

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This subject covers the fundamental theory of probability and statistical inference that is needed as a foundation for understanding and practice of the core methods of biostatistics, understood as the science of drawing conclusions from data in health and medical investigations. Major topics include fundamental concepts of probability and distributions, including simulation of hypothetical data, and key concepts of statistical estimation and hypothesis testing, including sampling variability, confidence intervals, likelihood functions and an introduction to the Bayesian approach to inference. The approach emphasizes a critical understand­ing of the role of statistical inference in health research.

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This subject extends the use of regression methods in biostatistics to include the analysis of frequency counts and event rates. Generalised Linear Models (GLMs) are proposed as tools for description, prediction and causal inference in health research. Students will learn how to propose, fit and interpret Poisson regression models for counts and rates, and Cox’s proportional hazards model for time-to-event data with right censoring. The Kaplan-Meier and Mantel-Cox estimators and the log-rank test for the survival function given lifetime data will be discussed alongside regression methods for the analysis of survival data. Mathematical concepts covered include maximum likelihood estimation, the likelihood ratio test for model comparison and how GLMs provide a unifying theory for the analysis of the frequency of events occurring over time using logistic, Poisson and Cox regression.

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This subject provides the foundation for understanding and using regression methods in biostatistics. Students will learn what a regression model is and how regression methods are used for the major purposes of research investigations: description, prediction and causal inference. The emphasis will be on learning how to build, fit and interpret regression models for these different purposes, focusing on linear regression for continuous outcomes and logistic regression for binary outcomes, and including treatment of key issues such as model fit, parametrisation and interaction. Important underlying mathematical concepts will be covered, such as the method of least squares, maximum likelihood estimation and matrix algebra representation of multiple regression.

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The epidemiology of infectious diseases differs from chronic disease - cases may be the source of infection for further cases, immunity is an important factor in disease transmission and control, and there is often the need for urgency in the detection and response to disease.

This subject introduces students to the strategies used to identify and respond to infectious disease problems. Content is updated weekly to incorporate topical infectious disease events, and emphasis is given to a practical understanding and application of infectious disease epidemiology. Students will learn the basic principles of infectious disease surveillance and response, and will develop the terminology, and written and oral skills for effective reporting. Students will also develop problem-solving skills in scenario-based exercises.

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This subject introduces students to health economics as a sub-discipline of economics. Students are provided with a comprehensive introduction to micro-economics, so the subject is suitable for students with no prior knowledge of economics. It paves the way for students to apply micro-economic concepts to the analysis of contemporary issues in public health and health care. Topics to be studied include the following:

• Introduction to economics, micro-economics and welfare economics
• Introduction to health economics
• The demand for health and health care
• The production and supply of health care; the economics of the health workforce
• Behavioural economics in health care
• The market for health care
• Market failure and the role of government in health care,
• Economics approaches to measuring equity in health and health care
• The Australian health care system analysed from an economic perspective

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This subject introduces students to a variety of sources of routinely collected health-related data and how these data are used to derive population measures of fertility, mortality and morbidity, and to measure health service utilisation, disease registration and reporting. You will also learn how to develop, design and deliver a valid and reliable health questionnaire, and how to design and implement a survey using an efficient sampling strategy, and to analyse and interpret the resulting data.

Topics include: 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 role of stratification, clustering and weighting.

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Statistical genomics is the application of statistical methods to understand genomes, their structure and function in many different scientific contexts, including: understanding biological mechanisms in health and disease and predicting outcomes. The course will also cover common statistical methods used to analyse whole-genome sequencing-based modern biological data, including genomics, transcriptomics, epigenomics and single-cell transcriptomics, as well as their application to population data to study causes of disease.

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As a scientist, it is not only important to be able to experiment, research and discover, it is also vital that you can communicate your research effectively in a variety of ways. Even the most brilliant research is wasted if no one knows it has been done or if your target audience is unable to understand it.

In this subject you will develop your written and oral communication skills to ensure that you communicate your science as effectively as possible. We will cover effective science writing and oral presentations across a number of formats: writing a thesis; preparing, submitting and publishing journal papers; searching for, evaluating and citing appropriate references; peer review, making the most of conferences; applying for grants and jobs; and using social media to publicise your research.

You will have multiple opportunities to practice, receive feedback and improve both your oral and written communication skills.

Please note: students must be undertaking their own research in order to enrol in this subject.

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Healthcare is information intensive. Health data are generated, shared, consumed, and stored in a variety of partially overlapping complex networks. Healthcare lags behind many other sectors, despite efforts to use digital technologies to shape and improve health data and information processes since the middle of the 20th Century. The need for digital transformation of health is driven by socio-economic concerns (making healthcare more accessible and affordable) and patient safety (reducing medical errors, and redundant and ineffective interventions).

This subject introduces the background, current state, and future opportunities of digital health. It provides a basic understanding of health and disease and how individuals experience both. It explores the nature of biomedical data, information, and knowledge - and how digital technologies are shaping the way these are used. Digital health technologies are examined from ethical, historical, technological, and psycho-social perspectives, considering positive and negative impacts.

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This subject covers statistical models for longitudinal and correlated data. Beginning with models based on normal distributions, the concept of hierarchical data structures is developed. Numerical and analytical examples are used to demonstrate the inadequacy of standard statistical methods, including the limitations of the repeated-measures analysis of variance. Extensions to non-normal data using generalised estimating equations (GEE’s) and generalised mixed linear models (GLMM’s) are explored using the R and Stata statistical software packages.

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AIMS

The subject introduces key topics in modern information organisation, particularly with regard to structured databases. The well-founded relational theory behind modern structured query language (SQL) engines, has given them as much a place behind the web site of an organisation and on the desktop, as they traditionally enjoyed on corporate mainframes. Topics covered may include: the managerial view of data, information and knowledge; conceptual, logical and physical data modelling; normalisation and de-normalisation; the SQL language; data integrity; transaction processing, data warehousing, web services and organisational memory technologies. This is a core foundation subject for both the Master of Information Systems and Master of Information Technology.

INDICATIVE CONTENT

This subject serves as an introduction to databases and data modelling from a data management perspective. Database design, from conceptual design through to physical implementation will be covered. This will include Entity Relationship modelling, normalisation and de-normalisation and SQL. Additionally the use of databases in various contexts will be explored (web based databases, connecting programs to databases, data warehousing, health contexts, geospatial databases).

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AIMS

The aim for this subject is for students to develop an understanding of approaches to solving moderately complex problems with computers, and to be able to demonstrate proficiency in designing and writing programs. The programming language used is Java.

INDICATIVE CONTENT

Topics covered will include:

• Java basics
• Console input/output
• Control flow
• Defining classes
• Using object references
• Programming with arrays
• Inheritance
• Polymorphism and abstract classes
• Exception handling
• UML basics
• Interfaces
• Collection & Generics
• Advanced Topics

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This subject introduces Bayesian statistical concepts and methods, with emphasis on practical applications in biostatistics. We begin with a discussion of subjective probability in quantifying uncertainty in the scientific process. Subsequently, the concept of full probability modelling is introduced and developed through single- and multi-parameter models with conjugate prior distributions. The connection with frequentist approaches is examined in light of the relationship between non-informative and informative prior distributions and their effect on posterior estimates We discuss the specification of appropriate prior distributions, including the concepts of non-informative and weakly informative priors. We consider the frequentist properties of Bayesian procedures. The application of Bayesian methods for fitting hierarchical models to correlated data structures is developed. Computational techniques for use in Bayesian statistics, especially the use of iterative simulation from posterior distributions using Markov chain Monte Carlo techniques (MCMC) will be covered using the Stan software through R.

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This unit covers modern statistical methods for assessing the causal effect of a treatment or exposure from randomised or observational studies. The unit begins by explaining the fundamental concept of counterfactual or potential outcomes and introduces causal diagrams and directed acyclic graphs (DAGs) to identify visually confounding, selection and other biases that prevent unbiased estimation of causal effects. Key issues in defining causal effects that can be estimated in a range of contexts are presented using the concept of the “target trial” to clarify exactly what the analysis seeks to estimate. A range of statistical methods for analysing data to produce estimates of causal effects are then introduced. Propensity score and related methods for estimating the causal effect of a single time point exposure are presented, together with extensions to longitudinal data with multiple exposure measurements, and methods to assess whether the effect of an exposure on an outcome is mediated by one or more intermediate variables. Comparisons will be made throughout with “conventional” statistical methods. Emphasis will be placed on interpretation of results and understanding the assumptions required to allow causal conclusions. Stata and R software will be used to apply the methods to real study datasets.

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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).

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Computing techniques and data mining methods are indispensable in modern statistical research and data science applications, where "Big Data" problems are often involved. This subject will introduce a number of recently developed methods and applications in computational statistics and data science that are scalable to large datasets and high-performance computing. The data mining methods to be introduced include general model diagnostic and assessment techniques, kernel and local polynomial nonparametric regression, basis expansion and nonparametric spline regression, and generalised additive models. Important statistical computing algorithms and techniques used in data science will be explained in detail. These include unsupervised learning of meaningful components, bootstrap resampling and inference, cross-validation, the Expectation-Maximisation (EM) algorithm and variational approximation, and Markov chain Monte Carlo methods including adaptive rejection and squeeze sampling, sequential importance sampling, slice sampling, Gibbs samplers and the Metropolis--Hastings algorithm.

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Topics include: 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 analysis 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.

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Faced with the rising cost of vaccines and increasing drug resistance, public health decision makers increasingly rely on epidemiological models of infectious disease transmission to predict the impact, and define optimal implementation of, intervention strategies. Such considerations are particularly critical in resource-constrained settings.

This subject introduces students to the concepts of infectious diseases modelling required to interpret modelling papers relevant to the public health context. By considering real world examples of the use of models to support practice, they will learn to distinguish between different types of modelling frameworks, and understand their relevance to alternative questions and settings. Building on their strengths in infectious diseases epidemiology, students will develop confidence in assessing whether model frameworks incorporate all relevant knowledge and are ‘fit for purpose’ to support decision making.

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Recent years have brought a rapid growth in the amount and complexity of health data captured. Among others, data collected in imaging, genomic, health registries and personal devices call for new statistical techniques in both predictive and descriptive learning. Machine learning algorithms for classification and prediction complement existing statistical tools in the analysis of these data. This unit will cover modern machine learning methods particularly useful for large and complex data. Topics include, classification trees, random forests, model selection, lasso, bootstrapping, cross-validation, generalised additive modelling, and regression splines. The statistical software R package will be used throughout the unit.

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This subject builds on methods and techniques learned in theoretical subjects by studying the application of statistics in real contexts. Emphasis is on the skills needed for a practising statistician, including the development of mature statistical thinking, organizing the structure of a statistical problem, the contribution to the design of research from a statistical point of view, measurement issues and data processing. The subject deals with thinking about data in a broad context, and skills required in statistical consulting.

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This subject is a core subject within the Master of Science (Epidemiology) and an elective within the Master of Public Health, the Master of Environment, and the Master of Biostatistics.

Epidemiology 2 is an in-depth exploration of research study design and development. Within this subject, students will learn contemporary approaches to designing studies for clinical and public health research and analysing their results.

Students will develop the skills to design and critique a variety of experimental and observational studies (cluster randomised controlled trials, randomised controlled trial variants, case control study variants and ecological studies). Complex causal diagrams will be introduced to assist with identifying confounders and assessing bias. Students learn how to apply quantitative bias analyses to quantify the direction and magnitude of bias in clinical and public health studies.

Additionally, the concept of a target trial will be introduced, and students will learn to apply this method in their future research. Alongside the target trial approach, students will learn the latest analytic strategies to control for confounding, including g-computation and inverse proportional weighting.

The subject will also cover the concept of effect-measure modification and how it differs from interaction, and its impact on external validity. By the end of the course, students will be able to estimate the potential effects of population and clinical interventions on a wider population.

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The aim of the capstone project is to deliver to the student a practical experience in a health and medical research work setting. The focus is on the application of knowledge and skills learnt during the core and elective coursework subjects. In this subject, students will learn to address the types of challenges that the practising biostatistician and their collaborator(s) typically face.

The statistical analysis of real data almost always requires compromises, since no single method is always guaranteed to be superior and more than one approach may be reasonable. Students will gain experience in applied, biostatistical work. This will require an iterative approach, involving refinement of the research question, planning and conducting statistical analyses, and presenting and discussing results and conclusions in close collaboration with subject-matter experts.

Effective communication with subject-matter experts is an important part of the biostatistician’s role in the workplace and, therefore, of the capstone project. Under the guidance of a biostatistical supervisor, students have the opportunity to work with subject-matter experts to apply appropriate statistical methods to answer proposed research questions. Students will give an oral presentation describing their methods and results, and provide a written report.

In this research project the student has the option for breadth or depth: two distinct research projects or one larger project with multiple challenges. Students must conduct their research over two consecutive semesters, commencing with Biostatistics Research Project Part 1 (POPH90288) followed by Biostatistics Research Project Part 2 (POPH90289).

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Please refer to POPH90288 Biostatistics Research Project Part 1.

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The aim of the capstone project is to deliver to the student a practical experience in a health and medical research work setting. The focus is on the application of knowledge and skills learnt during the core and elective coursework subjects. In this subject, students will learn to address the types of challenges that the practising biostatistician and their collaborator(s) typically face.

The statistical analysis of real data almost always requires compromises, since no single method is always guaranteed to be superior and more than one approach may be reasonable. Students will gain experience in applied, biostatistical work. This will require an iterative approach, involving refinement of the research question, planning and conducting statistical analyses, and presenting and discussing results and conclusions in close collaboration with subject-matter experts.

Effective communication with subject-matter experts is an important part of the biostatistician’s role in the workplace and, therefore, of the capstone project. Under the guidance of a biostatistical supervisor, students have the opportunity to work with subject-matter experts to apply appropriate statistical methods to answer proposed research questions. Students will give an oral presentation describing their methods and results, and provide a written report.

In this research project the student has the option for breadth or depth: two distinct research projects or one larger project with multiple challenges. Students must conduct their research in one semester. The alternative research project, POPH90288/POPH90289 is identical but spread over two consecutive semesters, whereas POPH90149 is limited in scope, ie less breadth and depth.

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The aim of the capstone project is to deliver to the student a practical experience in a health and medical research work setting. The focus is on the application of knowledge and skills learnt during the core and elective coursework subjects. In this subject, students will learn to address the types of challenges that the practising biostatistician and their collaborator(s) typically face.

The statistical analysis of real data almost always requires compromises, since no single method is always guaranteed to be superior and more than one approach may be reasonable. Students will gain experience in applied, biostatistical work. This will require an iterative approach, involving refinement of the research question, planning and conducting statistical analyses, and presenting and discussing results and conclusions in close collaboration with subject-matter experts.

Effective communication with subject-matter experts is an important part of the biostatistician’s role in the workplace and, therefore, of the capstone project. Under the guidance of a biostatistical supervisor, students have the opportunity to work with subject-matter experts to apply appropriate statistical methods to answer proposed research questions. Students will give an oral presentation describing their methods and results, and provide a written report.

In this research project a single research question is addressed. The alternative capstone option is the 25-point Biostatistics Research Project which runs across two semesters including Part 1 (POPH90288) and Part 2 (POPH90289) and allows more breadth or depth of research: two distinct research projects or one large project with multiple challenges.

View detailed information in the Handbook

This subject covers statistical models for longitudinal and correlated data. Beginning with models based on normal distributions, the concept of hierarchical data structures is developed. Numerical and analytical examples are used to demonstrate the inadequacy of standard statistical methods, including the limitations of the repeated-measures analysis of variance. Extensions to non-normal data using generalised estimating equations (GEE’s) and generalised mixed linear models (GLMM’s) are explored using the R and Stata statistical software packages.

View detailed information in the Handbook