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.

The aim of this subject 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. In particular, students gain experience in data manipulation and management using two major statistical software packages (Stata and R) and acquire fundamental programme skills for efficient use of each of these software packages.

This subject 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, and 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 are used throughout to demonstrate key concepts.

This subject provides the foundation theory and methods needed for biostatisticians to apply and critically interpret statistical inference, the science of drawing conclusions from data that are subject to variability. Major topics include review of the key concepts of estimation including sampling variability and construction of confidence intervals; null hypothesis testing; methods of inference based on likelihood theory (Fisher and observed information, likelihood ratio, Wald and score tests); and an introduction to the Bayesian approach to inference. The approach will emphasise a critical understand¬ing of the role of statistical inference in health research.

This subject provides the foundation for regression modelling. Topics covered include: 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 indicator 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.

This subject 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, and 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 are used throughout to demonstrate key concepts.

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;
• Methods for paired data.

The exponential family of distributions;

• Generalized Linear Models (GLMs);
• Parameter estimation for GLMs;
• Inference for GLMs, including the use of score, Wald and deviance statistics (including residuals) for confidence intervals and hypothesis tests.

Binary variables and logistic regression models:

• Methods for assessing model adequacy;
• Nominal and ordinal logistic regression for categorical response variables with more than two categories;

Count data and Poisson regression models:

• Log-linear models.

Software:

• Fitting GLMs in Stata and R.

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).

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.

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.

Topics include: 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; sample size calculations for survival studies.

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.

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 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.

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, generalised additive models, classification and regression trees, forward stagewise and gradient boosting models. Important statistical computing algorithms and techniques used in data science will be explained in detail. These include the bootstrap resampling and inference, cross-validation, the EM algorithm and Louis method, and Markov chain Monte Carlo methods including adaptive rejection and squeeze sampling, sequential importance sampling, slice sampling, Gibbs sampler and Metropolis-Hastings algorithm.

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 are able to 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.

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.

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 modeling required to interpret modeling 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 modeling 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.

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.

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

It covers the main experimental and observational study designs used in epidemiological research: These will include randomised controlled trials including the variants of trials, cohort studies, case-control studies including the variants of nested case-control studies, case-cohort studies and case-crossover studies and ecological studies.

Causal diagrams are introduced as a unifying means for identifying confounding and selection bias and interpreting associations. Other topics include: measurement error, effect modification and validity of the findings.

Skills in critically appraising findings from research will build on the base of Epidemiology one. Students will apply their knowledge to designing studies with the aim of investigating topical problems in public health.

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).

AIMS

The aims 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
• Generics

Statistical genomics is the application of statistical methods to understand genomes, their structure, function and evolutionary history, in many different scientific contexts, including: understanding biological mechanisms in health and disease, predicting outcomes and identifying individuals and their relatedness. Bioinformatics is an overlapping term that suggests more emphasis on data management and software pipelines. The course will also cover aspects of Genomic epidemiology, an overlapping field in which statistical genomics methods are used with family or population data to study causes of disease.