Unit Outlines

Aim: 
On completion of this unit students should be able to follow the mathematical demonstrations and proofs used in biostatistics at Masters degree level, and to understand the mathematics behind statistical methods introduced at that level. The intention is to allow students to concentrate on statistical concepts in subsequent units, and not be distracted by the mathematics employed.
Content:
Basic algebra and analysis; exponential functions; calculus; series, limits, approximations and expansions; numerical methods; linear algebra, matrices and determinants.
BCA Code:
MBB 
Coordinator:
- semester 1: Dr Keith Dear, National Centre for Epidemiology and Population Health, Australian National University
- semester 2: A/Professor Julian Leslie, Department of Statistics, Macquarie University

Aim: 
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.
BCA Code:
PDT
Coordinator:
Dr Rory Wolfe, Dept of Epidemiology & Preventive Medicine, Monash University

Aim: 
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.
Content:
Topics include: 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; the role of epidemiology in health services research and policy.
BCA Code:
EPI
Coordinator: 
Most of the collaborating universities offer Epidemiology. If you wish to attend on-campus lectures, or if your home university offers EPI by distance, the c oordinator will depend on the university . The major distance offering for most most BCA students is Introduction to Epidemiology delivered by the University of Queensland.

Aim:
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:
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 stratification, clustering and weighting.
BCA Code:
HIS
Coordinator: 
Mr Kevin McGeechan, School of Public Health, University of Sydney

Aim: 
The aim of this course is to introduce students to essential concepts and tools required for the management and analysis of data using modern statistical software. Data management principles and concepts are developed using relational database software (Microsoft Access). Data manipulation, descriptive analyses and interpretation are introduced using SAS and Stata statistical software. Students will also acquire skills in data display, summary presentation and pattern recognition using these tools.
Content 
. Module 1 - Data Management Concepts 
. Module 2 - Introduction to Stata and SAS
. Module 3 - Data Management Using Stata and SAS

BCA Code:
DMC
Coordinator /s : 
Prof Cate D'Este, Mr Stephen Hancock; Centre for Clinical Epidemiology and Biostatistics (CCEB) , University of Newcastle

Aim:
To provide a strong mathematical and conceptual foundation in the methods of statistical inference, with an emphasis on practical aspects of the interpretation and communication of statistically based conclusions in health research.
Content:
Review of the key concepts of estimation, and construction of Normal-theory confidence intervals; frequentist theory of estimation including hypothesis tests; methods of inference based on likelihood theory, including use of Fisher and observed information and likelihood ratio; Wald and score tests; an introduction to the Bayesian approach to inference; an introduction to distribution-free statistical methods.
BCA Code:
PSI
Coordinator/s: 
Adrienne Kirby, NHMRC Clinical Trials Centre, University of Sydney

Aim:
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 agreement (kappa statistics, Bland-Altman agreement method, intraclass correlation); diagnostic tests (sensitivity, specificity, predictive values, ROC curves, likelihood ratio);  statistical process control (special and common causes of variation, Shewhart CUSUM and EWMA charts); and systematic reviews (process, estimating treatment effect, assessing heterogeneity, publication bias).
BCA Code:
CLB 
Coordinator:
Prof Annette Dobson, School of Population Health, University of Queensland

Aim: 
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.
Content: 
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, multiple tests and subgroup analyses, including adjustment of significance levels and P-values; reporting trial results and use of the CONSORT statement.
BCA Code: 
DES
Coordinator/s: 
Prof Phil Ryan, Dept of Public Health, University of Adelaide

Aim:
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.
Content: 
The method of least squares; regression models and related statistical inference; flexible nonparametric regression; analysis of covariance to adjust for confounding; multiple regression with matrix algebra; model construction and interpretation (use of dummy variables, parametrisation , interaction and transformations); model checking and diagnostics; regression to the mean; handling of baseline values; the analysis of variance; variance components and random effects.
Code:
LMR
Coordinator/s:
Prof John Carlin, School of Population Health, University of Melbourne;
Prof Andrew Forbes, Dept of Epidemiology & Preventive Medicine, Monash University

Aim: 
To enable students to use generalized linear models (GLMs) and other methods to analyse categorical data with proper attention to the underlying assumptions. There is an emphasis on the practical interpretation and communication of results to colleagues and clients who may not be statisticians.
Content:
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; generalized 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.
BCA Code:
CDA
Coordinator: 
Dr Mark Jones School of Population Health, University of Queensland

Aim: 
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.
Content:
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.
BCA Code: 
SVA
Coordinator: 
Dr Petra Graham, Department of Statistics, Macquarie University

Aim:
Bioinformatics addresses problems related to the storage, retrieval and analysis of information about biological structure. This unit provides a broad-ranging study of this application of quantitative methods in biology.
Content:
Biology basics; Population genetics; Web-based tools, data sources and data retrieval; The analysis of single and multiple DNA or protein sequences; Hidden Markov Models and their applications; Evolutionary models; Phylogenetic trees; Analysis of microarrays; Functional Genomics; Use of R in bioinformatics applications.
BCA Code: 
BIF
Coordinator: 
Prof Graham Wood, Department of Statistics, Macquarie University

Aim:
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:
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 generalized linear mixed models (GLMM); methods for count data.
BCA Code:
LCD
Coordinator/s: 
Prof Andrew Forbes, Dept of Epidemiology & Preventive Medicine, Monash University;
Prof John Carlin, School of Population Health, University of Melbourne

Aim:
This elective unit extends and enhances the concepts developed in Design of Experiments and RCTs. On completion, students have the knowledge and skills required at an advanced professional level to design and analyse clinical trials, including cross-over designs and equivalence trials, and to identify and implement statistical methods for trial monitoring and reporting, with appropriate knowledge of regulatory requirements.
Content: 
Methods in RCTs for determining: stopping rules for interim analyses (O'Brien-Fleming, Peto), spending functions, stochastic curtailment; statistical principles encountered in relation to aspects of regulatory guidelines (ICH, FDA, EMEA), and related to reports prepared for data safety and monitoring committees (DSMC); design and analysis of cross-over trials (period effects, interactions); equivalence and non-inferiority trials; problems of defining and using surrogate endpoints as alternatives to direct clinical outcomes. 
BCA Code:
ACT
Coordinator: 
A/Pof Val Gebski, NHMRC Clinical Trials Centre, University of Sydney

Aim:
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.
Content: 
Topics will 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 noninformative 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.
BCA Code:
BAY
Coordinator: 
Dr Lyle Gurrin, School of Population Health, University of Melbourne

Unit options:

• one-project unit - worth equivalent credit points to a single unit
• a two-project unit - worth equivalent credit points to 2 units
• available at the University of Queensland:
  a four-project unit - worth equivalent credit points to 4 units

The schedule of study for students will be determined on a case-by-case basis with the BCA Program Coordinator at the students' home university, based on student needs and goals.

Students choosing the one-project unit will need to make up credit points equal to the Masters Degree by choosing an elective

Aim: 
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.
Content:
The student will usually provide evidence of having met this goal by presenting a portfolio or thesis made up of a preface and project reports .
PLEASE NOTE: Adequate supervisory arrangements must be in place before students commence this unit. Students wishing to complete the Masters Degree should discuss options for WPP with the BCA program coordinator at their home university.

See here for WPP Guidelines (PDF - last updated - Aug 2007), containing information about structure, supervision and assessment.

BCA Code:
WPP
Coordinator:
Coordinator will depend on university