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Unit of study_

CLTR5007: Statistical Principles and Clinical Trials

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

Statistical principles and concepts required to design clinical trials and analyse trial results will be introduced, including the appraisal of the appropriateness of analyses appearing in previous trial reports. Concepts which will be developed include an introduction to hypothesis testing, confidence interval estimation and understanding of univariable and adjusted analyses. Students will undertake analyses of study data where outcomes are continuous, binary and time-to-event variables. Concepts and issues involved in performing landmark analyses and in identification of key prognostic variables and their interpretation in a clinical trials context will be introduced. The basis for and understanding of sample size calculations for clinical trials will be covered. Analyses will be performed using Excel and statistical software. SPSS software will be supported but students may use any package they are familiar with and have available. It is the student's responsibility to purchase the software. Details will be given at the beginning of the semester.

Unit details and rules

Unit code CLTR5007
Academic unit NHMRC Clinical Trials Centre
Credit points 6
Prohibitions
? 
None
Prerequisites
? 
CLTR5001
Corequisites
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator Liz Barnes, liz.barnes@sydney.edu.au
Type Description Weight Due Length
Assignment Assignment 2
Written assignment.
40% Formal exam period
Due date: 20 Nov 2023 at 23:59
Available for two weeks.
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Online task Quiz 1
Ten true/false, multiple choice and multiple answer questions.
10% Week 05
Due date: 28 Aug 2023 at 23:59
Available for one week.
Outcomes assessed: LO1 LO2
Assignment Assignment 1
Written assignment.
40% Week 10
Due date: 09 Oct 2023 at 23:59
Available for two weeks.
Outcomes assessed: LO1 LO3 LO2
Online task Quiz 2
Ten true/false, multiple choice and multiple answer questions.
10% Week 12
Due date: 23 Oct 2023 at 23:59
Available for one week.
Outcomes assessed: LO3 LO4

Assessment summary

A satisfactory performance in the assignments is required to pass this unit. Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy (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 sydney.edu.au/students/guide-to-grades.

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:

Late submissions may have marks deducted in accordance with the University of Sydney’s guidelines ... https://www.sydney.edu.au/policies/default.aspx?mode=folder&uri=4619697

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
Week 01 Statistical concepts Online class (10 hr) LO1
Week 02 Continuous outcomes Online class (10 hr) LO2
Week 03 Continuous outcomes Online class (10 hr) LO2
Week 04 Continuous outcomes Online class (10 hr) LO2
Week 05 Binary outcomes Online class (10 hr) LO3
Week 06 Binary outcomes Online class (10 hr) LO3
Week 07 Binary outcomes Online class (10 hr) LO3
Week 08 Time-to-event outcomes Online class (10 hr) LO4
Week 09 Time-to-event outcomes Online class (10 hr) LO4
Week 10 Time-to-event outcomes Online class (10 hr) LO4
Week 11 Sample size Online class (10 hr) LO5
Week 12 Sample size Online class (10 hr) LO5
Week 13 Critical appraisal Online class (10 hr) LO6

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. Understand the statistical principles behind the analysis and interpretation of trial data, including confidence intervals, hypothesis testing and p-values and appropriate presentation and interpretation of results.
  • LO2. Present and interpret summary data for continuous outcomes. Understand and test the assumptions required for analysis of continuous outcomes. Perform statistical comparisons of continuous outcomes between two groups. Perform and interpret adjusted analyses of continuous outcomes.
  • LO3. Present and interpret summary data for binary outcomes. Perform statistical comparisons of binary outcomes between two groups. Perform and interpret adjusted analyses of binary outcomes. Be aware of suitable analyses of binary outcomes when data are sparse.
  • LO4. Present and interpret summary data for time-to-event outcomes. Understand and test the assumptions required for analysis of time-to-event outcomes. Perform and interpret statistical comparisons of time-to-event outcomes between two groups. Perform and interpret adjusted analyses of time-to-event outcomes.
  • LO5. Understand the connection between statistical hypothesis testing and significance level, power and sample size. Perform sample size calculations for comparing continuous, binary and time-to-event outcomes in two groups. Understand and adjust for the impact of non-adherence to treatment and loss to follow-up in sample size calculations.
  • LO6. Critically appraise sample size calculations in a clinical trial protocol. Critically appraise and interpret statistical analyses of clinical trial data.

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.

No major changes have been made since this unit was last offered.

Unit materials: Please work through the notes, readings and exercises at your own pace, and the recorded lectures for modules 2-5, discussing concepts or questions on eLearning if you wish. Please note the recommended time to be spent on 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.