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.

This online unit that can be undertaken either face-to-face, fully online, or in intensive block mode, introduces the concept of clinical epidemiology and provides students with core skills in clinical epidemiology at an introductory level. The unit is aimed at clinician learners and as such some clinical experience is required. Topics covered include asking and answering clinical questions; basic and accessible literature searching techniques; study designs used in clinical epidemiological research; confounding and effect modification; sources of bias; interpretation of results including odds ratios, relative risks, confidence intervals and p values; applicability of results to individual patients; critical appraisal of clinical epidemiological research literature used to answer questions of therapy (RCTs and systematic reviews), harm, prognosis, diagnosis and screening; applicability of results to individual patients; and evidence-based use of health resources.

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.

The aim of this unit is to develop a strong mathematical and conceptual foundation in the methods of statistical inference, which underlie many of the methods utilised in subsequent units of study, and in biostatistical practice. The unit provides an overview of the concepts and properties of estimators of statistical model parameters, then proceeds to a general study of the likelihood function from first principles. This will serve as the basis for likelihood-based methodology, including maximum likelihood estimation, and the likelihood ratio, Wald, and score tests. Core statistical inference concepts including estimators and their ideal properties, hypothesis testing, p-values, confidence intervals, and power under a frequentist framework will be examined with an emphasis on both their mathematical derivation, and their interpretation and communication in a health and medical research setting. Other methods for estimation and hypothesis testing, including a brief introduction to the Bayesian approach to inference, exact and non-parametric methods, and simulation-based approaches will also be explored.

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.

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.

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.

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.

The aim of the unit is to teach the use of Generalised Linear Models or GLMs and Survival Analysis methods, with proper attention to the underlying assumptions of these models. The unit will teach how GLMs can be used to analyse count data using Poisson and Negative Binomial regression, how Logistic regression models can be applied to binary, multinomial, and ordinal data, and the use of GLMs with continuous data. The unit covers methods to analyse time to event survival data including the Kaplan Meier curve, the Cox proportional hazards model, and parametric accelerated failure time models. The unit will focus on methods to assess the model fit and diagnostics of GLMs and survival models, and the practical interpretation and communication of model results.

The aim of this unit is to lay the foundation of biostatistical modelling to analyse data from randomised or observational studies. These skills are essential for biostatistics in practice and will be used by students for the remainder of their Master of Biostatistics studies. This unit will introduce the motivation for different regression analyses and how to choose an appropriate modelling strategy. This unit will teach how to use linear regression to analyse continuous outcomes and logistic regression for binary outcomes. Emphasis will be placed on interpretation of results and checking the model assumptions. Stata and R software will be used to apply the methods to real study datasets.

The aim of the unit is to teach the use of Generalised Linear Models (GLMs) and Survival Analysis methods, with proper attention to the underlying assumptions of these models. The unit will teach how GLMs can be used to analyse count data using Poisson and Negative Binomial regression; how Logistic regression models can be applied to binary, multinomial, and ordinal data; and the use of GLMs with continuous data. The unit covers methods to analyse time to event survival data including the Kaplan Meier curve, the Cox proportional hazards model, and parametric accelerated failure time models. The unit will focus on methods to assess the model fit and diagnostics of GLMs and survival models, and the practical interpretation and communication of model results.

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.

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).

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.

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.

The aim of this unit is 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.Statistical genomics is the application of statistical methods to understand genomes, their structure, function and history, in many different scientific contexts, including understanding biological mechanisms in health and disease. Statistical genomics is characterised by large datasets, high-dimensional regression models, stochastic processes, and computationally-intensive statistical methods. We will use the statistical package R to perform regression-based analyses of genetic data.

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; application of Bayesian methods for fitting hierarchical models to complex data structures. R will be used for simulations and model fitting using MCMC routines.

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 (or directed acyclic graphs) to visually identify 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 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 apply the methods to real datasets.

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.

Throughout our lives, information about our health and the care we receive is recorded and stored across various health-related databases, e.g., hospital admissions, ambulance service, cancer registry. Data linkage is a process that brings together information from different databases about the same individual, family, place or event. This process creates a chronological sequence of health events or individual 'health story' that can be combined into a much larger story about the health of people. This information can be used for research or to improve health services. This unit is suitable for health services researchers, policy makers, clinical practitioners, biostatisticians and data managers. We explain how data linkage is conducted, illustrate how data linkage can be used for research, highlighting the advantages, and the dangers and pitfalls. We describe how to design linked data studies, outline the data management steps required before analysing the data, and discuss some of the methods and issues of analysing linked data. Students will have access to data from a real data linkage and will gain hands-on experience to develop their programming skills for handling large complex dataset

This unit of study will appeal to anyone wanting to write medical papers for conferences or journals, or to improve their paper writing skills. Students will work at their own pace through 9 modules covering research integrity, medical style, abstracts, presentations and posters, constructing a paper, data visualisation, manuscript submission, responding to reviewers' comments, post-publication research dissemination, and peer- reviewing a paper. This unit aims to teach students the principles of research integrity in writing for medical journals, typical issues they may face, and link to resources to help them maintain integrity through their publishing careers. It will guide them to reliable evidence-based resources to improve their conference abstract, presentation and poster design, and manuscript style and writing. Students will learn about reporting guidelines, common pitfalls in writing and presenting research, choosing a journal, keywords, improving tables and figures for manuscripts through open source software, copyright, writing cover letters and response letters to reviewers. Students will learn about measuring research impact and ways to improve research reach, dealing with the media and press releases, using social media in dissemination, digital archiving and basic skills needed to act as a peer-reviewer. This is an online unit, but those needing to study in block mode will do online study as well as a workshop.

This unit aims to develop a critical and comparative understanding of the history, theory and practice of health policy as well as provide students with an understanding of the main concepts and analytical methods of health economics and political economy. It gives an overview of the political choices and frameworks that shape decision making in health. By the end of this unit students will be able to: Define the boundaries and key features of health policy; Identify policy instruments and how they function; Understand the main frameworks used for analysing health policy, and different approaches and perspectives regarding setting priorities in health policy; Apply methods and principles of health economics e.g. resource scarcity, opportunity cost, efficiency and equity to practical real-life examples; Critically analyse the role of economic evidence in informing policy decisions in health decision-making in Australia.

Environmental epidemiology studies the effects of environmental exposures on health and wellbeing in the human population. The unit has a strong focus on epidemiological and statistical methods and applications including time-series and spatial analysis. Taking an eco-social approach, we broadly define the environment as anything external to the person including the physical, social, psychological and aesthetic environment and their interactions. This unit will provide students with a practical understanding of the research methods used to assess the exposure-response relationship between environmental hazards and health outcomes. Building on students knowledge of the environment - for example air, water, soil, climate, the built environment, the unit will cover study designs and methods of exposure assessment and statistical analysis used in assessing environmental health risks. Students will gain technical skills in data analysis and visualisation including spatial data involving Geographic Information Systems (GIS) for mapping and statistical analysis of exposures and health outcomes using the R Statistical Software. The unit will also explore future directions in the field of environmental epidemiology. Students will also learn about its importance in the Planetary Health framework when assessing global health risks and impacts in the context of climate change.

This unit of study is intended for students who have completed Epidemiology Methods and Uses (or an equivalent unit of study) at a credit or higher level. It is designed to extend students' practical and theoretical knowledge of epidemiology beyond basic principles and in particular to give them a practical understanding of how epidemiological principles and practices are used in real world settings. Students are given an opportunity to acquire some of the practical knowledge and skills needed to undertake epidemiological research and also to consolidate their critical appraisal skills.

The suite of epidemiological practices and methods unique to infectious diseases comprises a critical toolkit that is urgently needed by epidemiologists in our current pandemic era. This unit will provide students with a firm understanding of infectious disease processes, modes of transmission, and transmission dynamics in populations of diverse demographic characteristics. Students will learn a standardised framework of infectious disease epidemiology to understand how pathogens move through populations and from which we can derive key parameters such as the basic reproduction number, epidemic growth, epidemic thresholds, and herd immunity thresholds. We will also incorporate aspects of networks and ecology to understand the ways in which contacts, and other forms of interaction, between individuals or between individuals and vectors influence transmission dynamics. Finally, we will explore the ways in which various public health interventions can be used to arrest infection transmission within populations and how to monitor the effects of such interventions.

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.

This unit combines decision theory and more advanced health economic concepts to provide students with hands-on skills in specialised analysis methods, and modelling techniques, for evaluating healthcare options and reaching recommendations in the face of uncertainty. Students will calculate and analyse data from clinical studies, extrapolate clinical study results to other settings, and construct models that synthesise evidence (and expert opinion) from multiple sources. Specific topics of study include: decision trees; expected utility theory; sensitivity and threshold analysis; the value of information (including screening and diagnostic tests); the calculation and analysis of costs and quality-adjusted survival using individual patient data (including bootstrapping techniques); Markov processes and micro-simulation; and presenting and interpreting the results of (health economic) evaluations. Lectures are accompanied by practical exercises and readings. Students gain experience applying the methods presented in lectures via computer practicals using Excel and decision analysis software (TreeAge).

This unit of study introduces you to qualitative research in health, providing you with core concepts and skills. It is designed for beginners and people who want an advanced level introduction. Over the course of the unit we will address: What is qualitative research? How is it different from quantitative research? What is its history? What research problems can it address? How do I design a qualitative study? What are the different (and best) ways to generate data? How do you analyse qualitative data? Is methodology different to method? What are ontology and epistemology? What is reflexivity (and aren't qualitative researchers biased)? What are the ethical issues? What is good quality qualitative research? How can I use qualitative evidence in policy or practice? You will get practical experience and skills through carrying out an observation, participating in a focus group, conducting an interview, analysing data, arguing for qualitative research in health, and appraising the quality of published literature. You will hear from working qualitative researchers about how they use qualitative methods in their work. This unit will give you the skills and confidence to begin conducting and using qualitative research.

Visual Analytics aims to facilitate the data analytics process through Information Visualisation. Information Visualisation aims to make good pictures of abstract information, such as stock prices, family trees, and software design diagrams. Well designed pictures can convey this information rapidly and effectively. The challenge for Visual Analytics is to design and implement effective Visualisation methods that produce pictorial representation of complex data so that data analysts from various fields (bioinformatics, social network, software visualisation and network) can visually inspect complex data and carry out critical decision making. This unit will provide basic HCI concepts, visualisation techniques and fundamental algorithms to achieve good visualisation of abstract information. Further, it will also provide opportunities for academic research and developing new methods for Visual Analytic methods.

The focus of this unit is on understanding and applying relevant concepts, techniques, algorithms, and tools for the analysis, management and visualisation of data- with the goal of enabling discovery of information and knowledge to guide effective decision making and to gain new insights from large data sets.
To this end, this unit of study provides a broad introduction to data management, analysis, modelling and visualisation using the Python programming language. Development of custom software using the powerful, general-purpose Python scripting language; Data collection, cleaning, pre-processing, and storage using various databases; Exploratory data analysis to understand and profile complex data sets; Mining unlabelled data to identify relationships, patterns, and trends; Machine learning from labelled data to predict into the future; Communicate findings to varied audiences, including effective data visualisations.
Core data science content will be taught in normal lecture + tutorial delivery mode. Python programming will be taught through an online learning platform in addition to the weekly face-to-face lecture/tutorials. The unit of study will include hands-on exercises covering the range of data science skills above.

This course provides an introduction to deep machine learning, which is rapidly emerging as one of the most successful and widely applicable set of techniques across a range of applications. Students taking this course will be exposed to cutting-edge research in machine learning, starting from theories, models, and algorithms, to implementation and recent progress of deep learning. Specific topics include: classical architectures of deep neural network, optimization techniques for training deep neural networks, theoretical understanding of deep learning, and diverse applications of deep learning in computer vision.

This unit of study gives a comprehensive overview of post-relational data models and of latest developments in data storage technology.
Particular emphasis is put on spatial, temporal, and NoSQL data storage. This unit extensively covers the advanced features of SQL:2003, as well as a few dominant NoSQL storage technologies. Besides in lectures, the advanced topics will be also studied with prescribed readings of database research publications.

This unit of study provides a conceptual and practical introduction to the use of common platforms that manage large relational databases. Students will understand the foundations of database management and enhance their theoretical and practical knowledge of the widespread relational database systems, as these are used for both operational (OLTP) and decision-support (OLAP) purposes. The unit covers the main aspects of SQL, the industry-standard database query language. Students will further develop the ability to create robust relational database designs by studying conceptual modelling, relational design and normalization theory. This unit also covers aspects of relational database management systems which are important for database administration. Topics covered include storage structures, indexing and its impact on query plans, transaction management and data warehousing.
In this unit students will develop the ability to: Understand the foundations of database management; Strengthen their theoretical knowledge of database systems in general and relational data model and systems in particular; Create robust relational database designs; Understand the theory and applications of relational query processing and optimisation; Study the critical issues in data and database administration; Explore the key emerging topics in database management.

The current health data revolution promises transformative advancements in healthcare services and delivery. However, the data generated are vast and complex. Extracting actionable understanding requires cross-disciplinary engagement between data science with healthcare. This unit explores the computational technologies involved in integrating and making sense of the breath of health data, and their use in better understanding the patient. Students will understand the data challenges presented by the various assays in which patients are quantified, spanning genetic testing to organ imaging. Students will explore how computational and machine learning models can span health data to derive integrated understanding of the links and patterns across them. They will employ such models in performing diagnosis and forecasting disease progression and intervention outcomes, thus enabling personalised medicine and supporting clinical decision making. This unit will develop students' understanding of current healthcare challenges, how these can be framed as data science questions, and how they can engage and apply their knowledge in cross-disciplinary ventures to improve healthcare.

This unit of study critically examines research ethics in its wider context, from how research is structured to its dissemination. It explores the ethical underpinnings of a variety of research methods and their uses in
humans and non-human animals including the justifications for engaging in research, key concepts in research ethics and research integrity. The unit also briefly examines the history of research and the impact of research abuse on participants, both human and non-human animal.

This unit provides students with an overview of the ethical and political issues that underlie public health policy and practice. The unit begins with some fundamentals about the nature of public health. We then explore key concepts in public health ethics including equity, liberty, utility, justice, and solidarity, and consider different ways of reasoning about the ethics of public health. A range of current public health problems and issues are presented and discussed, including ‘lifestyle’ diseases, screening, public mental health, health communication, and pandemics. Throughout, the emphasis is on learning to make sound arguments about the ethical aspects of public health policy, practice and research. Students will be encouraged to ask questions, and to compare and debate competing answers to those questions. What is public health? To what extent should we each be free to engage in practices that harm our health? What is the proper role of the state in attempting to change the health of populations? What is equity and why does it matter (and why aren’t we doing more about it)? Most learning occurs in the context of five teaching interactive intensives and the assigned course readings, which focus on the development and application of reasoning skills.

This unit will facilitate students to critically review the ethical issues that underlie the delivery of healthcare. Students will explore: dominant theoretical approaches relevant to ethical reasoning in the clinical context; key ethical concepts in the clinical encounter (such as autonomy, professionalism and confidentiality); major contexts in which ethical issues arise in clinical practice (such as the start and end of life); and the role of clinical ethics consultation. The unit will also consider specific issues and populations within clinical practice, such as healthcare in underserved populations. This Unit is taught predominantly online. Depending on student interest, periodic interactive workshops will also be offered. These can be attended in person, or via Zoom (synchronously or asynchronously).

This unit of study introduces students to human research ethics in its wider context It explores the ethical underpinnings of the research endeavour including the justifications for engaging in research and research integrity The unit also briefly reviews the history of research and the impact of research abuse on human participants

Medicines save lives but they can be costly and can have serious adverse effects. Value-laden decisions are continuously being made at individual, institutional, national and international levels regarding the medicines we need, want and can afford. In this unit of study, we will explore and critique global and national policies and processes related to medicines, examining how research and development agendas are set; how medicines are assessed and evaluated; and how new technologies are translated into practice. We will also explore broader trends such as globalisation, commercialisation and changing consumer expectations. By the end of the course, students will understand the forces shaping the development, regulation, funding and uptake of medicines both nationally and internationally, and the political, ethical, legal and economic issues that are at stake. This course is designed to appeal to a wide range of students from ethics, law, public health, health care, policy, communications, economics, business, politics, administration, and biomedical science.

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 statistical analyses conducted by the student must include multivariable regression modelling.

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.