Outline

Overview

All models are wrong, but some are useful. In this unit, your statistical toolkit will be expanded to include modern techniques for tackling challenges that often exist in health research data, such as missing observations, non-linear effects, and confounding and correlation between observations. The methods for correlated data are relevant for analysing some epidemiological observational study designs (e.g., matched case-control studies, longitudinal studies with repeated measurements), and clinical trial designs (e.g. cluster RCTs, cross-over RCTs). Techniques to help assess the usefulness of a model will also be covered. Topics covered include fractional polynomials for non-linear effects; mixed or random effects and marginal models (e.g. GEE) for correlated data; multiple imputation for missing data; propensity score for confounding; and tools to assess model performance and classification.The focus of this unit is on the application of statistical methods using the statistical software package R.

Unit details and rules

Academic unit	Public Health
Credit points	6
Prerequisites ?	PUBH5217
Corequisites ?	None
Prohibitions ?	CEPI5310
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Shuvo Bakar, shuvo.bakar@sydney.edu.au
Guest lecturer(s)	Kylie-Ann Mallitt, kylie-ann.mallitt@sydney.edu.au
Lecturer(s)	Shuvo Bakar, shuvo.bakar@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Assignment	Assignment 3 Data analysis report	40%	Formal exam period Due date: 10 Jun 2025 at 23:59	3000 words equivalent
Outcomes assessed:
Online task	Reflections 5 x short reflections on key topics	10%	Multiple weeks	No pre-specified length
Outcomes assessed:
Assignment	Assignment 1 Data analysis report	20%	Week 06 Due date: 04 Apr 2025 at 23:59	1500 words equivalent
Outcomes assessed:
Assignment	Assignment 2 Data analysis report	30%	Week 09 Due date: 02 May 2025 at 23:59	2000 words equivalent
Outcomes assessed:
= AI allowed ?

Assessment summary

Three data analysis reports worth 20%, 30% and 40%.

Five reflective pieces worth 10% in total.

Detailed information for each assessment will be provided on Canvas

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Demonstrates the learning outcomes at an exceptional standard
Distinction	75 - 84	Demonstrates the learning outcomes at a very high standard
Credit	65 - 74	Demonstrates the learning outcomes at a good standard
Pass	50 - 64	Demonstrates the learning outcomes at an acceptable standard
Fail	0 - 49	Does not meet the learning outcomes of the unit to a satisfactory standard

For more information see guide to grades.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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 Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

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
Week 01	Data analysis with R	Tutorial (2 hr)
Week 01	Generalised Linear Models	Lecture (1 hr)
Week 02	Generalised Linear Models	Tutorial (2 hr)
Week 02	Fractional Polynomials	Lecture (1 hr)
Week 03	Fractional Polynomials	Tutorial (2 hr)
Week 03	Piecewise Regression	Lecture (1 hr)
Week 04	Piecewise Regression	Tutorial (2 hr)
Week 04	Missing Data	Lecture (1 hr)
Week 05	Missing Data	Tutorial (2 hr)
Week 05	Resampling Methods	Lecture (1 hr)
Week 06	Resampling Methods	Tutorial (2 hr)
Week 06	Prediction Modelling	Lecture (1 hr)
Week 07	Prediction Modelling	Tutorial (2 hr)
Week 07	Propensity Scores	Lecture (1 hr)
Week 08	Propensity Scores	Tutorial (2 hr)
Week 08	Mixed Models 1	Lecture (1 hr)
Week 09	Mixed Models 1	Tutorial (2 hr)
Week 09	Mixed Models 2	Lecture (1 hr)
Week 10	Mixed Models 2	Tutorial (2 hr)
Week 10	Mixed Models 3	Lecture (1 hr)
Week 11	Mixed Models 3	Tutorial (2 hr)
Week 11	Generalised Estimating Equations	Lecture (1 hr)
Week 12	Generalised Estimating Equations	Tutorial (2 hr)
Week 12	Cluster Randomised Controlled Trials	Lecture (1 hr)
Week 13	Cluster Randomised Controlled Trials	Tutorial (2 hr)
Week 13	Overview	Lecture (1 hr)

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.

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

LO1. Develop statistical analyses in R and choose appropriate functions and packages
LO2. Fit and interpret regression models with non-linear effects
LO3. Appropriately analyse data which have missing values
LO4. Understand the principles of resampling methods and identify situations where these methods are useful
LO5. Build prediction models and assess model performance
LO6. Implement propensity score methods for confounding adjustment
LO7. Fit and interpret models for correlated data
LO8. Identify appropriate advanced statistical techniques for a given analysis task

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

Alignment with Competency standards

Outcomes	Competency standards
	Public Health Dentistry - 3.a. critically evaluating scientific research and literature, products and techniques to inform evidence-based specialist practice, and 3.b. synthesising complex information, problems, concepts and theories. 4.1.a. historical and contemporary literature 4.1.b. the scientific basis of dentistry including the relevant biological, medical and psychosocial sciences 4.2.a. the epidemiology of oral health and disease 4.2.d. the analysis of oral health needs and services in community and public health settings.
	Public Health Dentistry - 3.a. critically evaluating scientific research and literature, products and techniques to inform evidence-based specialist practice, and 3.b. synthesising complex information, problems, concepts and theories. 4.1.a. historical and contemporary literature 4.1.b. the scientific basis of dentistry including the relevant biological, medical and psychosocial sciences 4.2.a. the epidemiology of oral health and disease 4.2.d. the analysis of oral health needs and services in community and public health settings.
	Public Health Dentistry - 3.a. critically evaluating scientific research and literature, products and techniques to inform evidence-based specialist practice, and 3.b. synthesising complex information, problems, concepts and theories. 4.1.a. historical and contemporary literature 4.1.b. the scientific basis of dentistry including the relevant biological, medical and psychosocial sciences 4.2.a. the epidemiology of oral health and disease 4.2.d. the analysis of oral health needs and services in community and public health settings.
	Public Health Dentistry - 3.a. critically evaluating scientific research and literature, products and techniques to inform evidence-based specialist practice, and 3.b. synthesising complex information, problems, concepts and theories. 4.1.a. historical and contemporary literature 4.1.b. the scientific basis of dentistry including the relevant biological, medical and psychosocial sciences 4.2.a. the epidemiology of oral health and disease 4.2.d. the analysis of oral health needs and services in community and public health settings.
	Public Health Dentistry - 3.a. critically evaluating scientific research and literature, products and techniques to inform evidence-based specialist practice, and 3.b. synthesising complex information, problems, concepts and theories. 4.1.a. historical and contemporary literature 4.1.b. the scientific basis of dentistry including the relevant biological, medical and psychosocial sciences 4.2.a. the epidemiology of oral health and disease 4.2.d. the analysis of oral health needs and services in community and public health settings.
	Public Health Dentistry - 3.a. critically evaluating scientific research and literature, products and techniques to inform evidence-based specialist practice, and 3.b. synthesising complex information, problems, concepts and theories. 4.1.a. historical and contemporary literature 4.1.b. the scientific basis of dentistry including the relevant biological, medical and psychosocial sciences 4.2.a. the epidemiology of oral health and disease 4.2.d. the analysis of oral health needs and services in community and public health settings.
	Public Health Dentistry - 3.a. critically evaluating scientific research and literature, products and techniques to inform evidence-based specialist practice, and 3.b. synthesising complex information, problems, concepts and theories. 4.1.a. historical and contemporary literature 4.1.b. the scientific basis of dentistry including the relevant biological, medical and psychosocial sciences 4.2.a. the epidemiology of oral health and disease 4.2.d. the analysis of oral health needs and services in community and public health settings.
	Public Health Dentistry - 3.a. critically evaluating scientific research and literature, products and techniques to inform evidence-based specialist practice, and 3.b. synthesising complex information, problems, concepts and theories. 4.1.a. historical and contemporary literature 4.1.b. the scientific basis of dentistry including the relevant biological, medical and psychosocial sciences 4.2.a. the epidemiology of oral health and disease 4.2.d. the analysis of oral health needs and services in community and public health settings.

Responding to student feedback

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

The day of the lectures and tutorials will be changed to allow more preparation time before the tutorial. The material covering mixed models will be covered over three weeks.

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) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

PUBH5218: Advanced Statistical Modelling

Semester 1, 2025 [Online] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links