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Unit of study_

BSTA5012: Longitudinal and Correlated Data (LCD)

Semester 1, 2024 [Online] - Camperdown/Darlington, Sydney

This unit aims to enable students to apply appropriate methods to the analysis of data arising from longitudinal (repeated measures) epidemiological or clinical studies, and from studies with other forms of clustering (cluster sample surveys, cluster randomised trials, family studies) that will produce non-exchangeable outcomes. Content covered in this unit includes: paired data; the effect of non-independence on comparisons within and between clusters of observations; methods for continuous outcomes; normal mixed effects (hierarchical or multilevel) models and generalised estimating equations (GEE); role and limitations of repeated measures ANOVA; methods for discrete data; GEE and generalised linear mixed models (GLMM); methods for count data.

Unit details and rules

Unit code BSTA5012
Academic unit Public Health
Credit points 6
Prohibitions
? 
None
Prerequisites
? 
BSTA5210 or BSTA5211 or (BSTA5007 and BSTA5008)
Corequisites
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator Erin Cvejic, erin.cvejic@sydney.edu.au
Type Description Weight Due Length
Assignment Assignment 2
Written assessment covering Modules 4 to 6 content
30% Formal exam period
Due date: 05 Jun 2024 at 23:59
8-10 pages
Outcomes assessed: LO1 LO2 LO3 LO4
Online task Online quizzes
Non-assessed online quizzes
0% Multiple weeks ~100-200 words
Outcomes assessed: LO1 LO4 LO3 LO2
Assignment Module 1 - 2 assessment
Short answer questions based on Module 1 and 2 content
20% Week 04
Due date: 25 Mar 2024 at 23:59
6-8 pages
Outcomes assessed: LO1 LO2
Assignment Assignment 1
Written assessment covering Modules 1 to 3
30% Week 07
Due date: 15 Apr 2024 at 23:59
8-10 pages
Outcomes assessed: LO1 LO3 LO2
Assignment Module 4 - 5 assessment
Short answer questions based on Module 4 and 5 content
20% Week 11
Due date: 13 May 2024 at 23:59
6-8 pages
Outcomes assessed: LO2 LO4 LO3

Assessment summary

  • Module 1-2 Assessment will require submission of short-answers to selected exercises
  • Assignment 1 is a written assessment and will cover Modules 1 to 3.
  • Module 4-5 Assessment will require submission of short-answers to selected exercises
  • Assignment 2 is a written assessment and will cover all Modules (1 to 6) with a focus on Modules 4 to 6.

Additional details about 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. 

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:

This unit adheres to standard BCA policy for late penalties for submitted work, i.e. 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.

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
Multiple weeks Module 1: Introduction to correlated data using paired data and simple clustered data Individual study (20 hr) LO1 LO2
Module 2: Overview of different correlated and longitudinal data structures and related research questions Individual study (20 hr) LO1 LO2 LO3 LO4
Module 3: Methods for continuous outcome measures based on generalised estimating equations (GEE) Individual study (20 hr) LO1 LO2 LO3 LO4
Module 4: Methods for continuous outcome measures based on normal mixed models, with likelihood-based estimation. Individual study (20 hr) LO1 LO2 LO3
Module 5: Methods for discrete data: GEE and generalized linear mixed models (GLMM) Individual study (20 hr) LO1 LO2 LO3
Module 6: Methods for count data; transition models Individual study (20 hr) LO1 LO2 LO3

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. Recognise the existence of correlated or hierarchical data structures, and describe the limitations of standard methods in these settings
  • LO2. Develop and analytically describe appropriate models for longitudinal and correlated data based on subject matter considerations
  • LO3. Be proficient at using statistical software packages (Stata and R) to fit models and perform computations for longitudinal data analyses, and to correctly interpret results
  • LO4. Express the results of statistical analyses of longitudinal data in language suitable for communication to medical investigators or publication in biomedical or epidemiological journal articles

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.

LCD was last delivered in Semester 1 2023. Apart from new assessment tasks, there have been only minor changes since that delivery in the form of correcting typographical errors and minor edits for greater clarification of the text. An extension of coding examples done in R to complement existing Stata code was completed in 2022

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

Software requirements: For this subject you will need to have access to, and a working familiarity with, either Stata or R. All methods in this unit can be conducted using Stata alone or R alone, however, the set of examples given with Stata code is more complete than the set of examples with R code.

Students using Stata will need at least version 13 that was released in July 2013. The current version is Stata 18 released in April 2023. For R, the notes assume you are working with the latest version, although slightly earlier versions should not have any important differences. The latest version of R is R 4.3.2 "Eye Holes" released in October 2023.

Required mathematical background: No additional mathematical background is required beyond what is covered in the pre-requisites Mathematical Foundations for Biostatistics (MFB), Principles of Statistical Inference (PSI), and Regression Modelling for Biostatistics 1 (RM1).

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.