Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Postgraduate Coursework
- Location
- External
- Attendance mode
- Online
- Units
- 2
- Administrative campus
- Herston
- Coordinating unit
- Public Health School
This course will provide in-depth exposure to the fundamental concepts underpinning clinical epidemiology and practical skills to apply the commonly used statistical methods. In this course, students will develop an understanding of the following areas of interest to clinical epidemiologists: diagnosis, prognosis and treatment, and systematic review and meta-analysis.
This course aims to teach the core principles and methods of Clinical Epidemiology. Aᅠvariety of commonly used statistical methods will be taught, whichᅠassist evidence-based decision-making aroundᅠdiagnosis, prognosisᅠand treatment/interventions, with the aim of maximisingᅠpatients' health outcomes.
Course requirements
Assumed background
Basic statistical and epidemiologic proficiency is assumed. Knowledge of common statistical software would be an advantage.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
PUBH7630 and PUBH7600 or PUBH7650
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
PUBH7611
Incompatible
You can't enrol in this course if you've already completed the following:
STAT7605
Course contact
Course staff
Lecturer
Timetable
Additional timetable information
Lectures: Tuesday, 9:00-10:20hr
Workshops: Tuesday, 10:30-11:50hr
Aims and outcomes
This course aims to teach a variety of commonly used data analytical methods in Clinical Epidemiology that assist evidence-based decision-making around diagnosis, prognosis and treatment procedures and interventions likely to maximize the magnitude and quality of patients' health outcomes.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Generate critical research questions from clinical scenarios, and identify appropriate study designs to answer the questions
LO2.
Demonstrate an understanding of the principles, calculations and interpretation of measures of agreement and reliability
LO3.
Demonstrate an understanding of principles underlying the study of diagnostic tests, and apply this understanding to calculate and interpret diagnostic test characteristics
LO4.
Demonstrate the ability to select and use the appropriate statistical methods to analyse data from clinical studies, and interpret the results
LO5.
Demonstrate the ability to critically evaluate the validity of diagnostic, prognostic and treatment studies
LO6.
Demonstrate the ability to perform meta-analyses using appropriate pooling methods, and interpret the results to generate evidence-based recommendations
Assessment
Assessment summary
Category | Assessment task | Weight | Due date |
---|---|---|---|
Presentation |
Assessement 1
|
20% |
24/03/2025 2:00 pm |
Tutorial/ Problem Set |
Assessment 2
|
20% |
11/04/2025 2:00 pm |
Tutorial/ Problem Set |
Assessment 3
|
20% |
16/05/2025 2:00 pm |
Paper/ Report/ Annotation |
Assessment 4
|
40% |
9/06/2025 2:00 pm |
Assessment details
Assessement 1
- Identity Verified
- Mode
- Product/ Artefact/ Multimedia
- Category
- Presentation
- Weight
- 20%
- Due date
24/03/2025 2:00 pm
- Learning outcomes
- L01
Task description
Students will choose from the clinical diagnosis scenarios provided, write an appropriate research question to address the scenario, conduct a thorough literature search, then choose and describe a paper relevant to their topic and question.
A copy of the ppt slides and search strategy will need to be submitted along with the video presentation file.
Submission guidelines
Details will be provided in Blackboard.
Deferral or extension
You may be able to apply for an extension.
Please see 10. Policies & Guidelines
Late submission
Please see 10. Policies & Guidelines
Assessment 2
- Online
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 20%
- Due date
11/04/2025 2:00 pm
- Learning outcomes
- L02, L03
Task description
Problem set will consist of multiple choice, matching, fill in the blanks and short answer questions (some involving calculations and data analyses) related to Reproducibility and Diagnosis course content (up to Week 5).
Submission guidelines
Details will be provided in Blackboard.
Deferral or extension
You may be able to apply for an extension.
Please see 10. Policies & Guidelines
Late submission
Please see 10. Policies & Guidelines
Assessment 3
- Online
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 20%
- Due date
16/05/2025 2:00 pm
- Learning outcomes
- L04, L05
Task description
Problem set will consist of multiple choice, matching, fill in the blanks and short answer questions (some involving calculations and data analyses) about Prognosis and Treatment (covered Weeks 6-9).
Submission guidelines
Details will be provided in Blackboard.
Deferral or extension
You may be able to apply for an extension.
Please see 10. Policies & Guidelines
Late submission
Please see 10. Policies & Guidelines
Assessment 4
- Online
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 40%
- Due date
9/06/2025 2:00 pm
- Learning outcomes
- L05, L06
Task description
This assessment will involve critical appraisal, extracting data from individual studies, and performing a meta-analysis. The report will require students to consider appropriate presentation of their data analyses and results, as well as answer questions related to the interpretation of results and implications for practice.
Submission guidelines
Details will be provided in Blackboard.
Deferral or extension
You may be able to apply for an extension.
Please see 10. Policies & Guidelines
Late submission
Please see 10. Policies & Guidelines
Course grading
Full criteria for each grade is available in the Assessment Procedure.
Grade | Description |
---|---|
1 (Low Fail) |
Absence of evidence of achievement of course learning outcomes. Course grade description: (typically 0-19%) |
2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: (typically 20-44%) |
3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: (typically 45-49%) |
4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: (typically 50-64%) |
5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: (typically 65-74%) |
6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: (typically 75-84%) |
7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: (typically 85-100%) |
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Assignment Submission
Turnitin no longer automatically emails students a receipt when they upload an assignment.ᅠ Students need to download their receipt manually and keep a copy as proof of submission.ᅠ ᅠ
Assignments for this course will be submitted electronically via Blackboard and/or using Tunitin.ᅠ
Before submitted any assignments for this course you must ensure you have completed UQ’sᅠcompulsory onlineᅠAcademic Integrity Tutorial.
When you successfully submit your assessment, you need to manually download your receipt and keep a copy as proof of submission as Turnitin no longer automatically emails students a receipt when an assignment is uploaded.ᅠ ᅠ
It is the responsibility of the student to check the assignment preview and confirm that the assignment has been successfully submitted.ᅠᅠ
If the submission was not successful:
- Note the error message (preferably take a screenshot)
- Go to your assignment page and see if it is possible to submit again
- If you cannot submit again, immediately email your course coordinator.ᅠ
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
Learning period | Activity type | Topic |
---|---|---|
Multiple weeks From Week 1 To Week 13 |
Workshop |
Workshops Workshops will be held every week following the lecture. Learning outcomes: L01, L02, L03, L04, L05, L06 |
Week 1 (24 Feb - 02 Mar) |
Lecture |
Course Introduction Scope and definitions, main aims and activities, assessment details, searching for evidence Learning outcomes: L01 |
Week 2 (03 Mar - 09 Mar) |
Lecture |
Reproducibility 1 Introduction to measures of reproducibility for categorial outcomes. Introduction to Stata. Learning outcomes: L02 |
Week 3 (10 Mar - 16 Mar) |
Lecture |
Reproducibility 2 Reproducibility for continuous outcomes. Measures of agreement (Bland & Altman plots and limits of agreements, SEM) and reliability (ICC and Kappa coefficients) Learning outcomes: L02 |
Week 4 (17 Mar - 23 Mar) |
Lecture |
Diagnosis 1 Diagnostic test characteristics. Single and multiple (sequential and parallel) testing situations. Optimal cut-off points. Analyses for measuring diagnostic test accuracy. Learning outcomes: L03 |
Week 5 (24 Mar - 30 Mar) |
Lecture |
Diagnosis 2 Sources of bias in diagnostic studies. Critical appraisal of diagnostic study methods and reporting. Learning outcomes: L03, L05 |
Week 6 (31 Mar - 06 Apr) |
Lecture |
Prognosis 1 Prognosis clinical questions and study design, bias in prognostic studies, survival analysis (time to event outcomes) Learning outcomes: L04, L05 |
Week 7 (07 Apr - 13 Apr) |
Lecture |
Prognosis 2 Survival analyses continued. Identifying and adjusting for confounding. Learning outcomes: L04, L05 |
Week 8 (14 Apr - 20 Apr) |
Lecture |
Treatment/Therapy 1 Randomised controlled trials (RCTs). Design principles and methods for analyses of dichotomous outcomes. Learning outcomes: L04, L05 |
Week 9 (28 Apr - 04 May) |
Lecture |
Treatment/Therapy 2 Analysing continuous outcomes in RCTs. Bias and confounding in treatment studies. Learning outcomes: L04, L05 |
Week 10 (05 May - 11 May) |
Lecture |
Systematic Reviews & Meta-analysis 1 Synthesising the evidence - Systematic Reviews and Meta-Analyses. Principles of SR and MA design. Risk of bias assessment. Learning outcomes: L05, L06 |
Week 11 (12 May - 18 May) |
Lecture |
Systematic Reviews & Meta-analysis 2 Data extraction and pooling of dichotomous outcomes. Performing meta-analyses in Stata. Learning outcomes: L05, L06 |
Week 12 (19 May - 25 May) |
Lecture |
Systematic Reviews & Meta-analysis 3 Data extraction and pooling of continuous outcomes. Performing meta-analyses in Stata. Learning outcomes: L05, L06 |
Week 13 (26 May - 01 Jun) |
Lecture |
Systematic Reviews and Meta-analysis 4 Identifying and dealing with bias in systematic reviews and meta-analyses. Learning outcomes: L05, L06 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
Course guidelines
School of Public Health (SPH) Guidelines for late submission of progressive assessment - Preamble
To apply for an extension to the due date for a piece of progressive assessment (eg assignments, oral presentations and computer-based assignments) students should complete the online request at https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/applying-assessment-extension?p=2#2
Information regarding deferral of in-semester exams and end-of-semester exams is available from https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/deferring-exam
If requesting an extension on medical grounds, a medical certificate must be provided. The extension will be approved for the number of days included in the medical certificate that the student was not fit to study or work, eg if the medical certificate is for 3 days, an extension will be approved for 3 days maximum regardless of the student's request.
If requesting an extension using a Student Access Plan (SAP) as evidence, a maximum of 7-day extension will be approved in the first instance. Updated medical documentation, as well as a copy of the SAP, is required if requesting an extension for more than 7 days.
The maximum time for an in-semester extension is four weeks.
The following SPH guidelines are consistent with the UQ policy. However, the SPH Guidelines contain specific rules and interpretations for SPH courses, and requests for extension and penalties for late submissions will be judged according to the guidelines outlined in this document. You should read both the information in your my.UQ at the following link: https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/applying-assessment-extension and the SPH guidelines (below) before submitting a request for an extension. The SPH Guidelines apply to all courses offered by the School of Public Health unless the Course Profile explicitly states otherwise.
SPH Guidelines for late submission of progressive assessment
Initial extension for an individual item of assessment – the SPH Teaching & Assessment Support Team and/or the Course Coordinator decides.
This could be for medical or compassionate reasons, or if, in the opinion of the Course Coordinator, there are exceptional circumstances.
Acceptable and unacceptable reasons for an extension are listed at the following link, along with the required evidence to be provided: https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/applying-assessment-extension?p=1#1
All requests should be lodged at least 24 hours prior to the due date for the assessment.
If applying for an extension after the due date and time of the assessment item, your request may not be approved. An explanation as to why your request was not submitted prior must be included.
If approved, a new due date will be set. This would generally be no later than 7 days after the original due date, however this can be modified to take account of the circumstances of the request and the time that would have been lost from studies.
If the new due date is past the date for submission of end-of-semester results, the student will receive an INC (incomplete) result.
Second and all subsequent extensions for an individual item of assessment – the SPH Teaching & Assessment Support Team and/or the Program Director together with the Course Coordinator decides.
This would only be approved for exceptional circumstance with supporting documentation.
- Online requests must be made at least 24 hours prior to the due date from the first extension.
- The SPH Teaching & Assessment Support Team and/or the Course Coordinator will consult with the Program Director, who will make the final decision.
- If approved, the new due date would generally be no later than 7 days after the first extension due date.
- The Program Director should consider if remedial or other support should be offered to the student.
- The Program Director should provide a report on these matters as needed at SPH Examiners’ Meetings.
Please Note: In order to support course progression, extensions that total more than 14 calendar days from the original due date of an assessment item will only be approved in very exceptional circumstances. These requests are assessed and approved or denied on a case-by-case basis.
If you have been ill or unable to attend class for more than 14 days, we advise you to carefully consider whether you are capable of successfully completing your courses this semester. You might be eligible to withdraw without academic penalty.
Penalty for late submission
Submission of assignments, practical reports, workbooks, and other types of written assessments after the due date specified in the Course Profile will receive a penalty.
The penalty will be a deduction of 10% RELATIVE PERCENTAGE per day (24 hour period or part thereof, including weekends and public holidays) or for work graded on a 1-7 scale, a deduction of one grade per day, e.g. If the original mark is 73%, then 10% relative percentage is 10% of this value, ie 7.3%, The final mark for this assessment item after applying the penalty for 1 day late submission would be 73 -7.3 = 65.7% The same outcome is achieved by multiplying the original score by .9; ie 73 x .9 = 65.7%
The penalty for multiple days late is the relative percentage multiplied by the number of days late.
A submission that is not made within 10 days of the due date will receive a mark of 0% for that assessment item.
Where a student has sought more than one extension, the due date for calculating the penalty will be the due date for the most recently approved extension.
Submission of Medical Certificates
Students are responsible for ensuring that any medical documentation they submit is authentic and signed by a registered medical practitioner. Such practitioners can be identified via the AHPRA website. Also note that:
- Not all online medical services are staffed by registered practitioners
- If the registration status of the practitioner cannot be verified, then an alternative practitioner should be sought
- Students will be held fully responsible for all documentation they submit, even if done so in ignorance of the practitioner's registration status
Medical documentation may be subjected to an audit by the University.
SPH Assessment Guidelines
The School of Public Health assessment tasks have been designed to be challenging, authentic and complex. While students may us AI technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference AI use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI tools.