Skip to main content
Unit outline_

BSTA5006: Design of Randomised Controlled Trials

Semester 2, 2021 [Online] - Camperdown/Darlington, Sydney

The aim of this unit is to enable students to understand and apply the principles of design and analysis of experiments, with a particular focus on randomised controlled trials (RCTs), to a level where they are able to contribute effectively as a statistician to the planning, conduct and reporting of a standard RCT. This unit covers: ethical considerations; principles and methods of randomisation in controlled trials; treatment allocation, blocking, stratification and allocation concealment; parallel, factorial and crossover designs including n-of-1 studies; practical issues in sample size determination; intention-to-treat principle; phase I dose-finding studies; phase II safety and efficacy studies; interim analyses and early stopping; multiple outcomes/endpoints, including surrogate outcomes, multiple tests and subgroup analyses, including adjustment of significance levels and P-values; missing data; reporting trial results and use of the CONSORT statement.

Unit details and rules

Academic unit Public Health
Credit points 6
Prerequisites
? 
BSTA5001 and (BSTA5011 or PUBH5010 or CEPI5100)
Corequisites
? 
None
Prohibitions
? 
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 1
Written assignment covering Modules 1 and 2
30% Week 03
Due date: 31 Aug 2021 at 23:59
4-5 pages
Outcomes assessed: LO1 LO2 LO3
Assignment Assignment 2
Written assignment covering Modules 3 and 4
30% Week 07
Due date: 26 Sep 2021 at 23:59
4-5 pages
Outcomes assessed: LO4 LO5
Assignment Assignment 3
Written assignment covering Modules 1 to 5
40% Week 12
Due date: 01 Nov 2021 at 23:59
8-10 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5

Assessment summary

  • Assignment 1 covers Modules 1 and 2
  • Assignment 2 covers Modules 3 and 4
  • Assignment 3 covers all Modules (1 to 5)
  • Detailed information for each assessment can be found 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. This grade should not be used in cases where a student attempts all assessment tasks but fails to achieve a mandated minimum standard in one or more task. In such cases a Fail (FA) grade and a mark less than 50 should be awarded.

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 established by the faculty. This grade, with corresponding mark, should also be used in cases where a student fails to achieve a mandated standard in a compulsory assessment, thereby failing to demonstrate the learning outcomes to a satisfactory 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.

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
Week -02 Module 1 - Randomisation Independent study (20 hr) LO1
Week 01 Module 2 - Design of RCTs Independent study (20 hr) LO2 LO3
Week 03 Module 3 - Sample Size Independent study (20 hr) LO5
Week 05 Module 4 - Phase I and Phase II Studies Independent study (30 hr) LO4
Week 08 Module 5 - Analysis and Reporting of RCTs / Multiple Comparisons Independent study (30 hr) LO2 LO4

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

Textbook: Matthews, J.N.S. Introduction to Randomized Controlled Clinical Trials, (Second Edition) Chapman & Hall/CRC Texts in Statistical Science

It will be necessary for you to have access to this book, as some of the course material is contained in the text book.

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. Identify the benefits of randomisation as a mechanism for reducing bias, and implement a variety of randomisation schemes
  • LO2. Demonstrate knowledge of the principles behind the common experimental designs
  • LO3. Describe the efficiency advantages of crossover designs, and be able to design and interpret the two-period crossover study
  • LO4. Demonstrate an understanding of the principles underlying Phase I, II, III and IV studies, as well as an appreciation of the scientific basis underlying issues in clinical studies including intention-to-treat, blinding, interim analyses, subgroup analyses and the handling of missing data
  • LO5. Appreciate the importance of sample size in clinical studies, and perform sample size calculations for a variety of trial designs with different outcomes

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 unit was last delivered in Semester 2 2020. Major changes were made to DES in 2007 and 2008 and more recently in 2015 following a BCA curriculum review. Additional changes have been made in response to student feedback. Sections in the notes that generated a large amount of discussion have been updated, as have the reading materials and software programs made available. In 2018 there has been a change in Module 3 (sample size estimation), with the section on non-compliance revised following discussion with senior trialists, as well as updates to Stata code. Following recent student feedback, additional videos will be provided to support student learning.

This unit is delivered externally as part of the Biostatistics Collaboration of Australia.

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.