School of Public Health and Community Medicine

Advanced Biostatistics & Statistical Computing (PHCM9517)

image - Advanced Biostatistics & Statistical Computing


At the end of this course students will be able to apply advanced biostatistical methods to their public health and clinical research and gain the required statistical skills to write a journal article or a standard report. In particular, students will be able to correctly select the appropriate statistical analytical method to address specific research questions, conduct the selected statistical analysis using SAS software for statistical analysis, present and interpret the results appropriately and draw valid and insightful conclusions. The broad topics that will be covered in this course include: one-way analysis of variance, simple and multiple linear regression analysis, model building strategies in regression analysis to adjust for confounding and dealing with effect modification; advanced analysis of categorical data (analysis of KxK tables), logistic regression analysis for binary outcome data, regression analysis for count data (Poisson and Negative binomial regression), analysis of time to event data including life table, Kaplan-Meier survival plot, log rank test and Cox proportional Hazards model. The learning method will include formal lectures on the topics, hands-on problem solving tutorials, computer laboratory sessions to demonstrate the use of SAS software and guest presentation on the use of the methods in clinical and public health research.

Credit points

This course is an elective course in the postgraduate programs within the School of Public Health and Community Medicine, comprising six units of credit towards the total required for completion of the study program.

A prerequisite for this course is PHCM9498 Epidemiology and Statistics for Public Health or equivalent.

Mode of study

External (Distance) and Internal (Face-to-Face) classes on campus.

Course aim

This course aims to enable you to apply advanced biostatistical methods to address public health research questions. In particular, it aims to support you to reach a level of proficiency where you will be able to select the appropriate statistical analytical method to address specific research questions with a given data set, conduct the selected statistical analysis using SAS, present and interpret the results appropriately, and draw valid and insightful conclusions about the research question. 

Course Outcomes
Upon successful completion of the course you will be able to:
  • Determine the appropriate statistical analytical technique for different epidemiological study designs and datasets.
  • Conduct statistical analysis using advanced techniques on complex datasets with different types of variables.
  • Demonstrate an understanding of issues arising from the application of modelling techniques in statistical analysis and appropriate procedures to handle these issues. Key issues include: confounding and effect modification in epidemiological studies, model building strategies and model diagnostics.
  • Correctly interpret results and draw valid conclusions addressing the research question.
  • Critically discuss results and present findings at a standard that is sufficient for submission to scientific journals or reports.
Learning and teaching rationale

The course focuses on developing practical experience that will assist your understanding and application of statistical techniques and in using Stata software. The focus is to provide you with the capacity to think critically about epidemiological questions and the use of advanced biostatistical methods to address questions in medical and public health research.

Teaching strategies

Optional Residential Workshop: It is highly recommended for all students (internal and external) to attend a one-day workshop during residential week. It is highly recommended because the residential workshop introduces you to the course, its outcomes, assessment criteria, and the first module on one-way analysis of variance (ANOVA), and simple linear regression. In addition, the workshop is an opportunity for you to clarify expectations, meet other fellow students and establish a working relationship with the teaching staff. Importantly, during the second part of the workshop there will be a hands-on practical demonstration of Stata in the computer lab. In this session you will be introduced to the Stata program where you will learn important basic procedures of this program and how to use it for conducting ANOVA and simple linear regression.

You will find it useful to prepare for the workshop by reviewing this course outline and reading the course notes for the Module 1 as well as the notes on an ‘Introduction to Stata’.

Please ensure you have activated your z-pass before attending the workshop so that you can log onto the University system for the tutorial.


1. Take home test
Weighting: 15%

2. Assignment 1 - Research Project
Weighting: 45%

3. Assignment 2
Weighting: 40%

Learning resources

Learning resources for this course consist of the following:

  1. Course notes and recommended readings from journal articles and text books.  Specific reading lists are provided at the end of each module.
  2. Links to all the journal articles and available e-books from the recommended texts will be embedded on Moodle.
  3. Links to the scanned copies of relevant chapters from other books will also be available on Moodle.  
  4. Stata software for students - Stata 16 will be available in the Wallace Wurth computer lab G6/G7 and all the students enrolled in this course will have 24/7 access to those labs. Stata is also available on the hot desk computers in room 212 of Samuels building and coursework students can access them during the work hours.