Students

Search

Site Map
 
 
  UNIT OUTLINES
 

Following is a list of all units of study (subjects, courses, papers) comprising the curriculum for the program of courses in biostatistics.

For full unit details, download the BCA Program Outline

Note: when enrolling you will need to use the unit of study code used by your home university. See here for links to university postgraduate course information.



Epidemiology (EPI)
Mathematical Background for Biostatistics (MBB) 
Probability and Distribution Theory (PDT) 
Health Indicators and Health Surveys (HIS)
Data Management and Statistical Computing (DMC)
Principles of Statistical Inference (PSI)
Clinical Biostatistics (CLB)
Design of Randomised Controlled Trials (DES) 
Linear Models (LMR)
Categorical Data and GLMs (CDA)
Survival Analysis (SVA)
Workplace Project Portfolio (WPP) 
Longitudinal and Correlated Data (LCD)
Bioinformatics (BIF)
Bayesian Statistical Methods (BAY) 

 

Epidemiology (EPI)

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 

Availability:
Semesters 1 and 2

Coordinators:
Coordinator will depend on university

Some consoritum universites may offer Epidemiology on campus (face-to-face) and/or by distance in at least one semester. Home university postgraduate advisors may offer students the option to enrol in Epidemiology at that university.

This is the only instance in the BCA curriculum where a choice for study options may exist. All other BCA units are delivered by distance by one university only in any semester.

If students are not doing EPI at their home university, they will be doing (Introduction to) Epidemiology at the University of Queensland.

Prerequisites:
none


Mathematical Background for Biostatistics (MBB)

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  

Availability:
Semesters 1 and 2

Coordinator/s

Prerequisites:
none

 

Probability and Distribution Theory (PDT)

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

Availability:
Semesters 1 and 2 

Coordinator/s

Prerequisite:
MBB

Health Indicators and Health Surveys (HIS) 

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 

Availability:
Semester 1

Coordinator
/s

Co/prerequisite:
MBB

Data Management and Statistical Computing (DMC) 

Aim:  
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 SAS)
  • 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
  • Learn key principles regarding confidentiality and privacy in data storage, management and analysis

Content  
The topics covered are:

  • Module 1 – Stata and SAS: The basics (importing and exporting data, recoding data, formatting data, labelling variable names and data values; using dates, data display and summary presentation)
  • Module 2 – Stata and SAS: graphs, data management and statistical quality assurance methods (including advanced graphics to produce publication-quality graphs)
  • Module 3 – Data management using Stata and SAS (using functions to generate new variables, appending, merging, transposing longitudinal data; programming skills for efficient and reproducible use of these packages, including loops, arguments and programs/macros)

BCA code: 
DMC 

Availability:
Semesters 1 and 2

Coordinator/s


Prerequisites:
none

 

Principles of Statistical Inference (PSI)

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 

Availability:
Semesters 1 and 2

Coordinator/s

Prerequisites:
MBB, PDT

 

Clinical Biostatistics (CLB)

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 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).



BCA code: 
CLB  

Availability:
Semester 1

Coordinators
/s

Prerequisites:
EPI, MBB, PDT

Co/prerequisite:
PSI

 

Design of Randomised Controlled Trials (DES)

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:  
Topics incude: 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 

Availability:
Semester 2

Coordinators
/s

Prerequisites:
EPI, MBB

Linear Models (LMR)

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.

BCA code: 
LMR 

Availability:
Semesters 1 and 2

Coordinators/s

Prerequisites:
EPI, MBB, PDT

Co/prerequisite:
Program coordinator approval is required for taking EPI and LMR simultaneously.

NOTE: LMR is an important foundation unit. Students who do not develop a strong grasp of this material will struggle to become successful biostatisticians.

Categorical Data & GLMs (CDA)

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 

Availability:
Semester 2

Coordinator/s


Prerequisites:
EPI, MBB, PDT, PSI

Co/prerequisite:
LMR

 

Survival Analysis (SVA)

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 

Availability:
Semester 1

Coordinator/s


Prerequisites:
EPI, MBB, PDT, PSI, LMR

 

Biostatistics Research Project (WPP)*

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 containing information about structure, supervision and assessment. 

BCA code: 
WPP

Availability:
Semesters 1 and 2 - upon arrangement with program coordinator and the student's home university

Unit options:

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

Coordinator:  
Coordinator will depend on university

Prerequisites:
Minimum of 4 units, including LMR and DMC

*unit title differs across university; referred to as WPP in all BCA literature.

Longitudinal & Correlated Data (LCD)

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 

Availability:
Semester 1

Coordinator/s

Prerequisites:
EPI, MBB, PDT, PSI, LMR, CDA

 

Bioinformatics (BIF)

NOTE: BIF and BAY are delivered in alternate years. See here for delivery for the current year.

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; Use of R in bioinformatics applications. 

BCA code:  
BIF 

Availability:
Semester 2, biennial delivery

Prerequisites:
MBB, DMC, PDT, PSI, LMR

 

Bayesian Statistical Methods (BAY)

NOTE: BIF and BAY are delivered in alternate years. See here for delivery for the current year.

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 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 

Availability:
Semester 2, biennial delivery

Prerequisites:
EPI, MBB, PDT, PSI, LMR, CDA

top

 
 
   
Copyright © www.bca.edu.au
All rights reserved.
About us Courses Enrol Unis Contact Us