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Unit outline_

PUBH5217: Biostatistics: Statistical Modelling

Semester 2, 2024 [Online] - Camperdown/Darlington, Sydney

In this unit, you will learn how to analyse health data using statistical models. In particular, how to fit and interpret the results of different statistical models which are commonly used in medicine and health research: linear models, logistic models, and survival models. This unit is ideal for those who wish to further develop their research skills and/or improve their literacy in reading and critiquing journal articles in medicine and health. The focus of the unit is very applied and not mathematical. Students gain hands on experience in fitting statistical models in real data. You will learn how to clean data, build an appropriate model, and interpret results. This unit serves as a prerequisite for PUBH5218 Advanced Statistical Modelling.

Unit details and rules

Academic unit Public Health
Credit points 6
Prerequisites
? 
PUBH5018 or FMHU5002
Corequisites
? 
None
Prohibitions
? 
(PUBH5211 or PUBH5212 or PUBH5213)
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator Kylie-Ann Mallitt, kylie-ann.mallitt@sydney.edu.au
Tutor(s) Lucy Corbett, lucy.corbett@sydney.edu.au
The census date for this unit availability is 2 September 2024
Type Description Weight Due Length
Assignment Assignment 2
Written assessment
55% Formal exam period
Due date: 11 Nov 2024 at 23:59
10 pages or equivalent
Tutorial quiz Quiz 1
Online quiz
5% Week 04
Due date: 25 Aug 2024 at 23:59
10 questions
Outcomes assessed: LO1 LO8 LO3 LO2
Assignment Assignment 1
Written assessment
25% Week 06
Due date: 08 Sep 2024 at 23:59
5 pages or equivalent
Outcomes assessed: LO1 LO11 LO8 LO6 LO5 LO4 LO3 LO2
Tutorial quiz Quiz 2
Online quiz
5% Week 07
Due date: 15 Sep 2024 at 23:59
10 questions
Outcomes assessed: LO1 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Tutorial quiz Quiz 3
Online quiz
5% Week 10
Due date: 13 Oct 2024 at 23:59
10 questions
Outcomes assessed: LO1 LO11 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Tutorial quiz Quiz 4
Online quiz
5% Week 13
Due date: 03 Nov 2024 at 23:59
10 questions

Assessment summary

  • Four online quizzes - multiple choice (5% each).
  • Assignment 1: written statistical analysis report (25%)
  • Assignment 2: written statistical analysis report (55%)
  • Detailed information for each assessment can be found 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

Indicates work of an exceptional standard.

Distinction

75 - 84

Indicates work of a very high standard.

Credit

65 - 74

Indicates work of a good standard.

Pass

50 - 64

Indicates work of an acceptable standard.

Fail

0 - 49

Learning outcomes of the unit are not met to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

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.

Support for students

The Support for Students Policy 2023 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 2023. 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

WK Topic Learning activity Learning outcomes
Week 01 Introduction to R Lecture (1 hr) LO1 LO2
Week 02 Simple Linear Regression Lecture (1 hr) LO3 LO8
Introduction to R Tutorial (1.5 hr) LO1 LO2
Week 03 Multiple Linear Regression Lecture (1 hr) LO3 LO5 LO8
Simple Linear Regression Tutorial (1.5 hr) LO3 LO8
Week 04 Categorical Explanatory Variables Lecture (1 hr) LO4 LO11
Multiple Linear Regression Tutorial (1.5 hr) LO3 LO5 LO8
Week 05 Effect Modification Lecture (1 hr) LO6 LO8
Categorical Explanatory Variables Tutorial (1.5 hr) LO4 LO11
Week 06 Model Checking Lecture (1 hr) LO8
Effect Modification Tutorial (1.5 hr) LO6 LO8
Week 07 Model Building Lecture (1 hr) LO7
Model Checking Tutorial (1.5 hr) LO8
Week 08 Logistic Regression Lecture (1 hr) LO3 LO9
Model Building Tutorial (1.5 hr) LO7
Week 09 Model Checking for Logistic Regression Lecture (1 hr) LO3 LO9
Logistic Regression Tutorial (1.5 hr) LO3 LO9
Week 10 Introduction to Survival Analysis Lecture (1 hr) LO13 LO14 LO15
Model Checking for Logistic Regression Tutorial (1.5 hr) LO3 LO9
Week 11 Cox Proportional Hazard Model Lecture (1 hr) LO3 LO12 LO17
Introduction to Survival Analysis Tutorial (1.5 hr) LO13 LO14 LO15
Week 12 Model Checking for Cox Lecture (1 hr) LO16
Cox Proportional Hazard Model Tutorial (1.5 hr) LO3 LO12 LO17
Week 13 Other Regression Models Lecture (1 hr) LO10
Model Checking for Cox Tutorial (1.5 hr) LO16

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. read a data file into statistical software
  • LO2. manipulate and edit data set in statistical software
  • LO3. conduct appropriate exploratory data analysis when the outcome variable is continuous, binary, or time-to-event using statistical software
  • LO4. fit a multiple linear regression model using statistical software, where the explanatory variables may be continuous and/or categorical
  • LO5. interpret the results from a multiple linear regression model
  • LO6. identify the difference between a potential confounder and actual confounder
  • LO7. conduct appropriate model building strategies for building a regression model
  • LO8. use statistical software to conduct appropriate model checking for linear regression models
  • LO9. use logistic regression to assess the association between a binary outcome and multiple covariates
  • LO10. use Poisson regression to assess the association between a count outcome and multiple covariates
  • LO11. write statistical software to analyse categorical data, and correctly interpret the produced output
  • LO12. identify when it is appropriate to use survival analysis methods, including the logrank method and Cox proportional hazards model
  • LO13. define censoring, the survivor function, and the hazard function
  • LO14. produce survival curves in a plot using statistical software and correctly interpret such a plot
  • LO15. perform logrank tests and fit Cox proportional hazards models to analyse survival data
  • LO16. assess the proportional hazards assumption, and describe what to do if this assumption does not hold
  • LO17. write statistical software to analyse survival data, and correctly interpret the produced output.

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
LO1         
LO2         
LO3         
LO4         
LO5         
LO6         
LO7         
LO8         
LO9         
LO10         
LO11         
LO12         
LO13         
LO14         
LO15         
LO16         
LO17         

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

Weekly targeted summaries and streamlined lecture content have been developed in response to student feedback.

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.