Course Title: Advanced Biostatistics

Part A: Course Overview

Course Title: Advanced Biostatistics

Credit Points: 12.00

Course Code




Learning Mode

Teaching Period(s)

Course Coordinator: Assoc. Prof. Cliff da Costa

Course Coordinator Phone: +61 3 9925 6113

Course Coordinator Email:

Pre-requisite Courses and Assumed Knowledge and Capabilities

You must have completed Biostatistics (MATH1300) or an equivalent introductory statistics course in order to enrol in this course.

Course Description

The course provides you with an opportunity to learn advanced topics in Biostatistics that build on fundamental principles. The content covered will enable you to understand and analyse biomedical data from the perspective of research design and analysis. Topics addressed include:

  • Advanced ANOVA techniques including two-way ANOVA, MANOVA, and assessment of both fixed and random effects;
  • Data analysis for clinical trials, incorporating cluster randomised trials, crossover trials, and associated inferential tests such as repeated measures;
  • Regression analysis including ordinal and logistic regression, and multiple logistic regression techniques;
  • Analysis of person-time data including incidence-rate data, survival analysis and proportional hazards;
  • Advanced epidemiological techniques regards to power and sample size analysis and meta-analysis.

Objectives/Learning Outcomes/Capability Development

On the completion of this course you will improve or gain capabilities in:

  • Personal and professional awareness: You will improve your understanding of the environments in which biomedical data is collected and analysed.
  • Knowledge and technical competence: You will improve your technical competence in designing research studies and analysing data pertaining to the biomedical context.
  • Problem solving: you will develop strategies for dealing with imperfect real world data.

Overview of Learning Activities

The course features a range of learning activities that actively engage you in content acquisition and skills development required to develop research methodologies and analyse data in a biomedical context.

These activities will include lectures, practical computer laboratory classes, and assignments related to the usage of simulation software. You will attend lectures where the underlying theory will be presented. Practical computer classes will reinforce the material covered in lectures, with added value through the usage of simulation software. Assignments will provide you with an opportunity to practice your problem solving skills, test your understanding, and exchange ideas with others. You will also have the opportunity to discuss your progress with teaching staff.

Overview of Learning Resources

You will have access to computer laboratories utilising data analysis software available in the school (e.g., SPSS, SAS, R). This course is taught through a mixture of classroom instruction, computer laboratory exercises and assignments.

You will have access to extensive course materials made available via the online RMIT Learning Hub (myRMIT), including digitised readings, lecture notes and a detailed study program, external internet links and access to RMIT Library online and hardcopy resources.

Overview of Assessment

Assessment will be based on computer lab exercises, assignments and an end of semester examination. Feedback on labs and tests will be provided to you during the semester.