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Unit outline_

CLTR5001: Introduction to Clinical Trials

Semester 1, 2023 [Online] - Camperdown/Darlington, Sydney

This unit of study will focus on the strengths and weaknesses of different clinical study designs. Designs considered will include cohort (retrospective and prospective), cross-sectional, case-control and randomized controlled designs. The different phases of clinical trial designs in the development of therapies will also be examined including phase I (first in man), phase II/pilot and phase III comparative designs. Extension and adaptation of randomized designs will also be covered including cluster and factorial designs and adaptive pilot studies. Students will gain the skills necessary to choose between these designs for best practice. Types of outcomes (continuous, categorical, time-to-event) will be discussed. Methods of allocating participants to interventions (randomization), as well blinding and allocation concealment will be covered together with aspects of protocol development. On completion of this unit, the student will be familiar with the differences between study types and study designs, as well as the principles and practice of randomisation. It is also expected that the candidate will be able to develop stratified randomisation schemes for their own studies.

Unit details and rules

Academic unit NHMRC Clinical Trials Centre
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator Adrienne Kirby, adrienne.kirby@sydney.edu.au
Tutor(s) Adrienne Kirby, adrienne.kirby@sydney.edu.au
Type Description Weight Due Length
Assignment Assignment 2
Written task
40% Formal exam period
Due date: 11 Jun 2023 at 23:59

Closing date: 18 Jun 2023
To be added by the unit coordinator
Assignment Assignment 1
Written assignment
40% Mid-semester break
Due date: 16 Apr 2023 at 23:59

Closing date: 23 Apr 2023
To be added by the unit coordinator
Outcomes assessed: LO1 LO11 LO10 LO9 LO8 LO7 LO6 LO5 LO3 LO2
Assignment Quiz 2
MCQ, short answer
10% Multiple weeks
Due date: 14 May 2023 at 23:59

Closing date: 21 May 2023
1-2 sentences per question
Assignment Quiz 1
Short answers, peer review
10% Multiple weeks
Due date: 19 Mar 2023 at 23:59

Closing date: 26 Mar 2023
1-2 sentences per question
Outcomes assessed: LO1 LO13 LO12 LO8 LO7 LO6 LO5 LO4 LO2

Assessment summary

This unit will be assessed by the use of two assignments and two quizzes. Each quiz will be worth 10%. The assignments will be worth 40% each. Please see the study timetable for when these are due. It is a requirement that both assignments be passed to pass this unit.
There will be a two week period between the assignments being made available and the final submission date. Quizzes will be available for one week.
Your assignment should be uploaded onto Canvas, with your student number as part of the identification: eg 123456_Assignment_1.doc. This is necessary because they are downloaded from Canvas to be marked, so are no longer associated with a particular student ID.
Please note: Assessment deadlines are important. Extensions may be granted for special circumstances. It is the student’s responsibility to discuss arrangements with the course coordinator PRIOR to the release of the assessment material.

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.

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:

It is expected that unless an application for Special Consideration (done centrally by the University) or Special Arrangements has been approved, students will submit all assessments for a unit of study on the specified due date. If the assessment is completed or submitted within the period of extension, no academic penalty will be applied to that piece of assessment. If an extension is either not sought, not granted or is granted but work is submitted after the extended due date, the late submission of assessment will result in an academic penalty as follows, unless otherwise stated in the course resolutions: • Late assignments that have not been granted extensions and are of a standard to receive a pass or higher mark will attract a penalty of 5% of the maximum mark per day late including weekend days (e.g. if the assignment is worth 40 marks, the penalty is 2 marks per day late) until the mark reaches 50% of the maximum mark (e.g. 20 marks if the maximum is 40 marks). • Assignments that are not of a pass standard will not have marks deducted and will fail regardless. • Assignments submitted more than 10 days late without prior approval from the unit of study coordinator will not be accepted and will be given a zero (0) mark.

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.

WK Topic Learning activity Learning outcomes
Weekly See canvas Online class (130 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11 LO12 LO13 LO14 LO15 LO16 LO17 LO18 LO19 LO20

Attendance and class requirements

Requirements for passing the unit: All students are required to successfully complete the University’s Academic Honesty Education Module in their first year of candidature. The module contains information and a number of quizzes. It can be accessed through the Canvas course list.

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

See canvas

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. understand which phase and/or type of study is required to address the primary question of any trial
  • LO2. understand basic statistical concepts of a trial design, such as sample size calculations and randomisation
  • LO3. understand the basic principles of protocol design and the key aspects that go into it
  • LO4. understand the different study designs (observational and experimental), the advantages and disadvantages of each, and when they are appropriate to use
  • LO5. understand the different types of study outcomes
  • LO6. understand how outcomes relate to study design
  • LO7. understand basic principles for sample size calculations
  • LO8. understand outcomes which are specific to oncology studies
  • LO9. understand the purpose, design and analysis, and interpretation of phase 1 and 2 clinical trials (including dose-finding)
  • LO10. show a basic understanding of phase 3 trials
  • LO11. understand the rationale for phase 4 clinical trials
  • LO12. understand the rationale for and the principle of phase 3 studies and RCTs
  • LO13. understand different study designs (parallel, crossover, etc.) and when they would be appropriate to use
  • LO14. understand the difference between superiority, non-inferiority and equivalence studies
  • LO15. understand the theory behind basic methods of randomisation and why randomisation is important
  • LO16. understand the role of less common approaches to randomisation
  • LO17. understand the important of blinding and the different types
  • LO18. understand methods of allocation concealment
  • LO19. understand the basic format, importance and purpose of a protocol
  • LO20. understand how the topics in this unit relate to the sections of a protocol.

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.

N/A

Work, health and safety

See canvas

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.