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Unit outline_

PUBH5218: Advanced Statistical Modelling

Semester 1, 2022 [Normal day] - Remote

All models are wrong, but some are useful. Developing a useful statistical model from the available data can be challenging! For example, what should you do if a model assumption is violated, or if data are missing? Your statistical toolkit will be expanded to include modern techniques for tackling challenging issues that often exist in health research data, e.g. missing observations, non-linear effects, confounding and correlation between observations in a dataset. 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. This unit of study focuses on the application of statistical methods using the statistical software R. Topics: 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; tools to assess model performance and classification.

Unit details and rules

Academic unit Public Health
Credit points 6
Prerequisites
? 
PUBH5212 or PUBH5217
Corequisites
? 
None
Prohibitions
? 
CEPI5310
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator Kylie-Ann Mallitt, kylie-ann.mallitt@sydney.edu.au
Type Description Weight Due Length
Assignment Assignment 3
Data analysis report
40% Formal exam period
Due date: 06 Jun 2022 at 23:59
3000 words equivalent
Outcomes assessed: LO1 LO7 LO8 LO2
Assignment Reflections
5 x short reflections on key topics
10% Multiple weeks No pre-specified length
Outcomes assessed: LO8
Assignment Assignment 1
Data analysis report
20% Week 07
Due date: 04 Apr 2022 at 23:59
1500 words equivalent
Outcomes assessed: LO1 LO8 LO2
Assignment Assignment 2
Data analysis report
30% Week 10
Due date: 02 May 2022 at 23:59
2000 words equivalent
Outcomes assessed: LO1 LO8 LO6 LO5 LO4 LO3

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.

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.

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

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

WK Topic Learning activity Learning outcomes
Week 01 Data analysis with R Tutorial (2 hr) LO1
Generalised Linear Models Lecture (1 hr) LO1
Week 02 Generalised Linear Models Tutorial (2 hr) LO1
Fractional Polynomials Lecture (1 hr) LO1 LO2
Week 03 Fractional Polynomials Tutorial (2 hr) LO1 LO2
Piecewise Regression Lecture (1 hr) LO1 LO2
Week 04 Piecewise Regression Tutorial (2 hr) LO1 LO2
Missing Data Lecture (1 hr) LO1 LO3
Week 05 Missing Data Tutorial (2 hr) LO1 LO3
Resampling Methods Lecture (1 hr) LO1 LO4
Week 06 Resampling Methods Tutorial (2 hr) LO1 LO4
Prediction Modelling Lecture (1 hr) LO1 LO5
Week 07 Prediction Modelling Tutorial (2 hr) LO1 LO5
Propensity Scores Lecture (1 hr) LO1 LO6
Week 08 Propensity Scores Tutorial (2 hr) LO1 LO6
Week 09 Mixed Models 1 Lecture (1 hr) LO1 LO7
Week 10 Mixed Models 1 Tutorial (2 hr) LO1 LO7
Mixed Models 2 Lecture (1 hr) LO1 LO7
Week 11 Mixed Models 2 Tutorial (2 hr) LO1 LO7
Generalised Estimating Equations Lecture (1 hr) LO1 LO7
Week 12 Generalised Estimating Equations Tutorial (2 hr) LO1 LO7
Cluster Randomised Controlled Trials Lecture (1 hr) LO1 LO7
Week 13 Cluster Randomised Controlled Trials Tutorial (2 hr) LO1 LO7
Overview Lecture (1 hr) LO8

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 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

Alignment with Competency standards

Outcomes Competency standards
LO1
Public Health Dentistry - DBA
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.
LO2
Public Health Dentistry - DBA
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.
LO3
Public Health Dentistry - DBA
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.
LO4
Public Health Dentistry - DBA
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.
LO5
Public Health Dentistry - DBA
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.
LO6
Public Health Dentistry - DBA
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.
LO7
Public Health Dentistry - DBA
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.
LO8
Public Health Dentistry - DBA
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.

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

Lectures and tutorials will take place on seperate days of the week to allow time to complete exercises. The material covering mixed models will be covered over two weeks to accommodate public holidays. The assessments are more distributed throughout the semester to allow students to incorporate assessment feedback from one assignment to the next.

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.