Outline

Overview

This unit of study is intended for students who have completed CEPI5100 Introduction to Clinical Epidemiology or PUBH5010 Epidemiology Methods and Uses as well as an introductory biostatistics units (FMUH5002, PUBH5018 or BSTA5002). In addition, students are required to complete PUBH5217 or BSTA5210 as a co-requisite if they have not already completed these units of study. It is designed to extend students' methodological skills to a more advanced level in the design and analysis of research studies using real-world data. The unit will provide a firm grounding in causal inference methods incorporating how to use the target trial approach and how to construct directed acyclic graphs for improved analysis of observational data. Finally, the unit will incorporate teaching on prognosis research methods which will provide students with an understanding of how to interpret and design high quality prognostic factor research, contemporary approaches to building prognostic models and the application of prognostic models to personalised or stratified medicine.

Unit details and rules

Academic unit	Public Health
Credit points	6
Prerequisites ?	(CEPI5100 or PUBH5010) and (FMHU5002 or PUBH5018 or BSTA5002)
Corequisites ?	PUBH5217 or BSTA5210
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Fiona Stanaway, fiona.stanaway@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Research analysis	Assignment 2- Part 2 - Research analysis SAQ Short answer questions	30%	Formal exam period Due date: 17 Nov 2025 at 23:59	1500 words	AI allowed
Outcomes assessed:
Out-of-class quiz	Module 1 quiz Online quiz	2.5%	Week 02 Due date: 18 Aug 2025 at 23:59	30 mins	AI allowed
Outcomes assessed:
Out-of-class quiz	Module 2 quiz Online quiz	2.5%	Week 03 Due date: 18 Aug 2025 at 23:59	30 mins	AI allowed
Outcomes assessed:
Out-of-class quiz	Module 3 quiz Online quiz	2.5%	Week 04 Due date: 25 Aug 2025 at 23:59	30 mins	AI allowed
Outcomes assessed:
Out-of-class quiz	Module 4 quiz Online quiz	2.5%	Week 05 Due date: 01 Sep 2025 at 23:59	30 mins	AI allowed
Outcomes assessed:
Practical skill	Assignment 1 - Part 1 - Workshop Preparation Short answer questions	10%	Week 05 Due date: 05 Sep 2025 at 23:59	500 words	AI allowed
Outcomes assessed:
Out-of-class quiz	Module 6 quiz Online quiz	2.5%	Week 08 Due date: 22 Sep 2025 at 23:59	30 mins	AI allowed
Outcomes assessed:
Out-of-class quiz	Module 7 quiz Online quiz	2.5%	Week 09 Due date: 06 Oct 2025 at 23:59	30 mins	AI allowed
Outcomes assessed:
Experimental design	Assignment 1 - Part 2 - Experimental design SAQ Short answer questions	30%	Week 09 Due date: 07 Oct 2025 at 23:59	1500 words	AI allowed
Outcomes assessed:
Out-of-class quiz	Module 8 quiz Online quiz	2.5%	Week 10 Due date: 13 Oct 2025 at 23:59	30 mins	AI allowed
Outcomes assessed:
Out-of-class quiz	Module 9 quiz Online quiz	2.5%	Week 11 Due date: 20 Oct 2025 at 23:59	30 mins	AI allowed
Outcomes assessed:
Practical skill	Assignment 2 - Part 1 - Workshop preparation Short answer questions	10%	Week 11 Due date: 24 Oct 2025 at 23:59	500 words	AI allowed
Outcomes assessed:

Assessment summary

Module quizzes - module 1-4 and 6-9 have 10 quiz questions each that need to be completed by the due dates specified in the timetable in Canvas. Quizzes are timed (30 minutes) and you only get one attempt.
Assignment 1 - is split into two parts. Part 1 includes short answer questions worth 10 marks to be completed and submitted prior to workshop 1. Part 2 includes short answer questions worth 30 marks that is submitted after the workshop.
Assignment 2 - is split into two parts. Part 1 includes short answer questions worth 10 marks to be completed and submitted prior to workshop 2. Part 2 includes short answer questions worth 30 marks that is submitted after the workshop.

Assessment criteria

Result name	Mark range	Description
High distinction	85 - 100	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail	0 - 49	The learning outcomes of the unit of study have not been met to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Multiple weeks	Modules 1 -4	Independent study (48 hr)
Multiple weeks	Modules 6 - 9	Independent study (48 hr)
Week 06	Face to face workshop - Saturday 14th September	Workshop (8 hr)
Week 12	Face-to-face workshop - Saturday 1st November	Workshop (8 hr)

Attendance and class requirements

Participants are expected to attend the two face to face workshops.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Explain causal inference methods and how they improve analysis of observational data
LO2. Construct Directed Acyclic Graphs and use them to plan analysis of observational data
LO3. Design an observational study that uses instrumental variables
LO4. Apply the Target Trial approach to answering a causal question using observational data
LO5. Appraise the quality of studies producing prognostic models
LO6. Understand study designs used in stratified care research to better target the right care to the right patients

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This is the first time that this unit has been offered.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

CEPI5105: Advanced Epidemiology

Semester 2, 2025 [Block mode] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links