Outline

Overview

The goal of this capstone unit of study is to give students practical experience in a health and medical research work setting. The focus is on the application of the knowledge and skills learnt during the coursework Master's program. Students will learn to address the types of challenges that the practising biostatistician and their collaborators typically face. Projects can be created or provided in the student’s workplace or by a researcher, research group, or institution. Projects should be selected that allow students to consolidate the concepts and theories learnt throughout core and elective coursework units, providing providing opportunities to extend this knowledge, and be challenged as they would be in biostatisticalpractice. The project should involve analysing real-world data to answer one or more substantive research questions using appropriate statistical methods. The statistical analyses conducted by the student must will typically include multivariable regression modelling or a statistical problem of similar complexity. Given that effective communication is an important part of a biostatistician’s role in the workplace, students will give an oral presentation describing their methods and results and provide a written report.

Unit details and rules

Academic unit	Public Health
Credit points	6
Prerequisites ?	48 credit points including BSTA5004 and (BSTA5008 or BSTA5009 or BSTA5210 or BSTA5211)
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Farzaneh Boroumand, farzaneh.boroumand@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Assignment	Research Training Modules Canvas modules on research practice and ethics:RSCH9001, RSCH9002, RSCH9003	0%	Week 03 Due date: 16 Mar 2025 at 23:59	3 x 1 hour modules
Outcomes assessed:
Assignment	Statistical analysis plan Completion of a statistical analysis plan proforma	10%	Week 04 Due date: 23 Mar 2025 at 23:59	1500 words
Outcomes assessed:
Presentation	Oral presentation Audiovisual presentation	20%	Week 10 Due date: 05 May 2025 at 23:59	10-12 mins, 15 slides
Outcomes assessed:
Assignment	Research project portfolio Written reflective preface and project report	70%	Week 13 Due date: 01 Jun 2025 at 23:59	6000 words, 30-40 pages
Outcomes assessed:
= hurdle task ? = AI allowed ?

Assessment summary

Research training modules: Students are required to enroll in and complete three Canvas based research modules: RSCH9001 Responsible Research Practice (1 hour module), RSCH9002 Human Ethics Module - General (1 hour module), and RSCH9003 Human Ethics Module - Health and Medical (1 hour module)
Statistical analysis plan: populate a statistical analysis plan proforma with the details of the proposed analysis in sufficient detail to allow another biostatistican to perform the analyses.
Oral presentation: a presentation describing the research project, inlcuding the research questions, details of the analysis approach, results summary, limitations and methodological challenges, and current project status.
Research portfolio: a portfolio containing a reflective preface and a project report.

These assessments will be barrier tasks, and will be graded. Students must satisfy the requirements for all assessments to complete the unit of study. Marking rubrics and additional details for each assessment will be provided on Canvas.

Assessment criteria

Result name	Mark Range	Description
High Distinction	85-100	Demonstrates the learning outcomes at an exceptional standard
Distinction	75-84	Demonstrates the learning outcomes at a very high standard
Credit	65-74	Demonstrates the learning outcomes at a good standard
Pass	50-64	Demonstrates the learning outcomes at an acceptable standard
Fail	0-49	Does not meet the learning outcomes of the unit to a satisfactory standard

For more information see guide to grades.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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.

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.

Support for students

The Support for Students Policy 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. 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

Weekly schedule

WK	Topic	Learning activity	Learning outcomes
Ongoing	Research Project (as approved by the Unit Coordinator)	Project (140 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. Appreciate the challenges facing biostatisticians in professional practice
LO2. Demonstrate strategies for elicitation of pertinent research questions and associated biostatistical issues from a health or medical research project
LO3. Collaborate with a health or medical researcher in devising a strategy for statistical analysis of research data
LO4. Perform data integrity checks and an appropriate statistical analysis involving multivariable regression
LO5. Present results and interpretation of biostatistical analyses in a written report of a standard suitable for publication in an academic journal

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 since last delivery.

Additional information

Departmental permission is required to undertake this unit of study.
For Supervision (SU) mode, an appropriate project and supervisory arrangements must be in place before permission will be granted.

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

Use of generative artificial intelligence (AI) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

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

On this page

Useful links

BSTA5020: Biostatistics Research Project 1

Semester 1, 2025 [Supervision] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

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

On this page

Useful links