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Unit of study_

BSTA5211: Regression Modelling for Biostatistics 2 (RM2)

Semester 2, 2023 [Online] - Camperdown/Darlington, Sydney

The aim of the unit is to teach the use of Generalised Linear Models (GLMs) and Survival Analysis methods, with proper attention to the underlying assumptions of these models. The unit will teach how GLMs can be used to analyse count data using Poisson and Negative Binomial regression; how Logistic regression models can be applied to binary, multinomial, and ordinal data; and the use of GLMs with continuous data. The unit covers methods to analyse time to event survival data including the Kaplan Meier curve, the Cox proportional hazards model, and parametric accelerated failure time models. The unit will focus on methods to assess the model fit and diagnostics of GLMs and survival models, and the practical interpretation and communication of model results.

Unit details and rules

Unit code BSTA5211
Academic unit Public Health
Credit points 6
Prohibitions
? 
BSTA5008 and BSTA5009
Prerequisites
? 
(PUBH5010 or BSTA5011 or CEPI5100) and (BSTA5210 or BSTA5007) and BSTA5002
Corequisites
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator Gillian Heller, gillian.heller@sydney.edu.au
Lecturer(s) Ken Beath, ken.beath@sydney.edu.au
Type Description Weight Due Length
Assignment Assignment 1
Written assessment
30% -
Due date: 04 Sep 2023 at 23:59
8-10 pages
Outcomes assessed: LO1 LO2 LO3 LO6
Assignment Assignment 2
Written assessment
30% -
Due date: 02 Oct 2023 at 23:59
8-10 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Assignment Assignment 3
Written assessment and presentation.
40% -
Due date: 03 Nov 2023 at 23:59
8-10 pages plus presentation
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7

Assessment summary

  • Assignment 1 will be a written assignment covering Module 1 and Module 2
  • Assignment 2 will be a written assignment covering Module 3
  • Assignment 3 will be a written assignment and delivery of a presentation, covering content from Modules 1-5

Assessments will typically involve performing statistical computation exercises and analyses and producing a written report, along with the statistical code used to conduct the analyses and appropriately formatted output. 

Further details of assessments will be provided on Canvas.

Assessment criteria

Grade

Mark Range

Description

AF

Absent fail

Range from 0 to 49

To be awarded to students who fail to demonstrate the learning outcomes for the unit at an acceptable standard through failure to submit or attend compulsory assessment tasks or to attend classes to the required level. In cases where a student receives some marks but fails the unit through failure to attend or submit a compulsory task, the mark entered shall be the marks awarded by the faculty up to a maximum of 49. 

FA

Fail

Range from 0 to less than 50

To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard 

PS

Pass

Range from 50 to less than 65

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard

CR

Credit

Range from 65 to less than 75

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard

D

Distinction

Range from 75 to less than 85

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard

HD

High distinction

Range from 85 to 100 inclusive

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional 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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

The standard BCA policy for late penalties for submitted work is a 5% deduction from the earned mark for each day the assessment is late, up to a maximum of 10 calendar days (including weekends and public holidays).

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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
Multiple weeks Module 1: Generalised Linear Models and the Exponential Family of Distributions Individual study (20 hr) LO1 LO6 LO7
Module 2: GLMs for continuous outcomes, counts, and rates; and Logistic Models Individual study (30 hr) LO1 LO2 LO3 LO6 LO7
Module 3: Survival (time-to-event) analysis Individual study (30 hr) LO4 LO5
Module 4: The Cox Proportional Hazards model Individual study (20 hr) LO4 LO5 LO6 LO7
Module 5: Extensions of the Cox model and parametric survival models Individual study (20 hr) LO5 LO6 LO7

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.

Required readings

  • Vittinghoff E, Glidden D, Shiboski S, McCulloch C. Regression Methods in Biostatistics: Linear, logistic, survival and repeated measures models. 2nd Edition. Springer Verlag 2012

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. Explain the theory of generalised linear models (GLMs) and statistical inference based on GLMs
  • LO2. Analyse data using logistic regression models for binary, multinomial and ordinal categorical data
  • LO3. Analyse count and rate data using Poisson regression, Negative Binomial, Zero-Inflated models, and continuous data using GLMs
  • LO4. Explain the nature of survival data and summarise and display survival data using nonparametric methods, including the Kaplan-Meier curve
  • LO5. Analyse survival data using the Cox proportional hazards model, including time dependent covariates and the stratified Cox model
  • LO6. To assess and evaluate the model fit and diagnostics of GLMs and survival model
  • LO7. Synthesise results of analyses to present and communicate findings

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

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

This is the fourth delivery of RM2. The first deliveries have received positive feedback. We continue to revise and update the materials as any issues are raised. The course has been designed to present both R and Stata methods throughout the learning materials. Interactive tutorials will continue to be scheduled through the semester, and recorded videos covering key methods are also provided.

This unit is delivered as part of the Biostatistics Collaboration of Australia (BCA). 

Software requirements: For this subject you will need to have access to R or Stata. Code and output in the module notes are given in both R and Stata, and students may choose to work in either software language. Stata 17 was released in April 2021, and we assume you are using this version. However, some of you may be using Stata 15 or Stata 16. We are not aware of any major differences between Stata versions that affect the material, but minor issues will be pointed out in posts on Canvas. The most recent versions of R and RStudio are available to download:

https://www.r-project.org/  https://www.rstudio.com/products/rstudio/download/ 

Required mathematical background: Students who have undertaken the pre-requisites will have the required mathematical background for the course. This unit is practical in nature and is focused on the application of GLM and survival models on datasets using statistical software, with proper attention to the underlying assumptions.

Disclaimer

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

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