PUBH5217: Semester 2, 2022

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Units PUBH5217 Semester 2 2022 [Normal day]

Unit outline_

PUBH5217: Biostatistics: Statistical Modelling

Semester 2, 2022 [Normal day] - Camperdown/Darlington, Sydney

Overview

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

Type	Description	Weight	Due	Length
Assignment	Assignment 2 Written assessment	55%	Formal exam period Due date: 14 Nov 2022 at 23:59	To be added by unit coordinator
Outcomes assessed:
Tutorial quiz	Quiz 1 Online quiz	5%	Week 04	To be added by unit coordinator
Outcomes assessed:
Assignment	Assignment 1 Written assessment	25%	Week 06 Due date: 11 Sep 2022 at 23:59	To be added by unit coordinator
Outcomes assessed:
Tutorial quiz	Quiz 2 Online quiz	5%	Week 07	To be added by unit coordinator
Outcomes assessed:
Tutorial quiz	Quiz 3 Online quiz	5%	Week 10	To be added by unit coordinator
Outcomes assessed:
Tutorial quiz	Quiz 4 Online quiz	5%	Week 13	To be added by unit coordinator
Outcomes assessed:

Assessment summary

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
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t 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.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to R	Lecture (1 hr)
Week 02	Simple Linear Regression	Lecture (1 hr)
Week 02	Introduction to R	Tutorial (1.5 hr)
Week 03	Multiple Linear Regression	Lecture (1 hr)
Week 03	Simple Linear Regression	Tutorial (1.5 hr)
Week 04	Categorical Explanatory Variables	Lecture (1 hr)
Week 04	Multiple Linear Regression	Tutorial (1.5 hr)
Week 05	Effect Modification	Lecture (1 hr)
Week 05	Categorical Explanatory Variables	Tutorial (1.5 hr)
Week 06	Model Checking	Lecture (1 hr)
Week 06	Effect Modification	Tutorial (1.5 hr)
Week 07	Model Building	Lecture (1 hr)
Week 07	Model Checking	Tutorial (1.5 hr)
Week 08	Logistic Regression	Lecture (1 hr)
Week 08	Model Building	Tutorial (1.5 hr)
Week 09	Model Checking for Logistic Regression	Lecture (1 hr)
Week 09	Logistic Regression	Tutorial (1.5 hr)
Week 10	Introduction to Survival Analysis	Lecture (1 hr)
Week 10	Model Checking for Logistic Regression	Tutorial (1.5 hr)
Week 11	Cox Proportional Hazard Model	Lecture (1 hr)
Week 11	Introduction to Survival Analysis	Tutorial (1.5 hr)
Week 12	Model Checking for Cox	Lecture (1 hr)
Week 12	Cox Proportional Hazard Model	Tutorial (1.5 hr)
Week 13	Other Regression Models	Lecture (1 hr)
Week 13	Model Checking for Cox	Tutorial (1.5 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. 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
Learning outcomes

Responding to student feedback

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

Tutorials have been extended in length from 1 hour to 1.5 hours 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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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