CLTR5004: Semester 2, 2023

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Units CLTR5004 Semester 2 2023 [Online]

Unit outline_

CLTR5004: Advanced Trial Design

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

Overview

Candidates will be taught skills to design and interpret equivalence and non-inferiority trials, cluster randomised trials and crossover trials. Biomarker-based designs are also covered including enrichment, hybrid and novel designs such as basket, umbrella, and platform trials. The pros and cons of each design are outlined. The unit will equip students with the ability to determine the most appropriate and optimal design to answer a clinical research question. Special aspects relating to cross-over studies will be taught. The difference between prognostic and predictive factors will be illustrated. Techniques to validly incorporate composite end points and co-primary endpoints will be learnt. Skills to incorporate sub-studies into clinical research projects will be covered in this unit.

Unit details and rules

Academic unit	NHMRC Clinical Trials Centre
Credit points	6
Prerequisites ?	CLTR5001 and CLTR5007
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Rachel O'Connell, rachel.oconnell@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Assignment 2 Long Assignment	40%	Formal exam period Due date: 13 Nov 2023 at 23:59	9 pages max
Outcomes assessed:
Online task	Quiz 1 Short Assignment	10%	Week 05 Due date: 28 Aug 2023 at 23:59	2 pages max
Outcomes assessed:
Assignment	Assignment 1 Long Assignment	40%	Week 09 Due date: 02 Oct 2023 at 23:59	7 pages max
Outcomes assessed:
Online task	Quiz 2 Short Assignment	10%	Week 11 Due date: 16 Oct 2023 at 23:59	2 pages max
Outcomes assessed:
= hurdle task ?

Assessment summary

Both assignments must be passed in order to pass the course. 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	Module 1: trial designs - non‐inferiority and equivalence	Online class (10 hr)
Week 02	Module 1: trial designs - non‐inferiority and equivalence	Online class (10 hr)
Week 03	Module 2: trial designs - dealing with clusters	Online class (10 hr)
Week 04	Module 2: trial designs - dealing with clusters	Online class (10 hr)
Week 05	Module 3: trial designs - crossover studies	Online class (10 hr)
Week 06	Module 4: trial designs - complex and specialised designs	Online class (10 hr)
Week 07	Module 5: advanced endpoint decisions	Online class (10 hr)
Week 08	Module 5: advanced endpoint decisions	Online class (10 hr)
Week 09	Module 6: monitoring trial data	Online class (10 hr)
Week 10	Module 6: monitoring trial data	Online class (10 hr)
Week 11	Module 7: sub‐study design and implementation	Online class (10 hr)
Week 12	Module 7: sub‐study design and implementation	Online class (10 hr)
Week 13	Revision	Online class (10 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.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Determine the appropriate study design that will provide the best evidence to answer a clinical research question.
LO2. Understand the impact of non-compliance on design, analysis and interpretation of results.
LO3. Understand the difference between prognostic and predictive factors.
LO4. Have knowledge of biomarker-based designs including basket, platform, and umbrella. Additionally be aware of strategies such as adaptive randomisation to facilitate allocation into such designs.
LO5. Understand the different types of trial endpoints (composite, co-primary and surrogate) and the key issues associated with each.
LO6. Understand the role of the Independent Safety and Data Monitoring Committee and the statistical and ethical issues when monitoring accumulating data from clinical trials.
LO7. Understand the key principles involved in substudy design and implementation.

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.

No changes have been made since this unit was last offered

Additional information

Unit materials: Please work through the notes and papers at your own pace, discussing difficult concepts or questions as needed on Canvas. Reading journal articles and extracting the relevant information is an important skill, and not easy to develop. Please note the course schedule for each module and the assessment deadlines.

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

Additional information

Additional information

Disclaimer

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