STATS 768 : Longitudinal Data Analysis
Science
2025 Semester Two (1255) (15 POINTS)
Course Prescription
Course Overview
STATS 768 describes longitudinal data analysis. This is important for the modelling of repeated measurements, and part of the course will focus on their use in the analysis of epidemiological data. The course will begin with data exploration techniques and within-person summary statistics reflecting changes over time as well as data visualization.
Progressing to the exploration and applied regression modelling of longitudinal and clustered data, with a focus on the health sciences and use of clinical and cohort data. Including generalized estimating equation (GEE), generalized linear mixed models (GLMMs) plus sandwich estimators, survival analyses, estimating variation in slopes of mixed models, marginal structural models for causal inference. Emphasis will be placed on algorithms, likelihoods, drop-outs with the meaningfulness of missing data.
Students will be taught to program in both R and SAS and to be able to interpret the resulting output. The skills developed in this course are particularly useful for those wishing to have a career involving the analysis of epidemiological and clinical trial data collected in the health sciences, census information collected by the NZ government, data collected for biological experiments or become a biostatistician or global health analyst.
Capabilities Developed in this Course
| Capability 3: | Knowledge and Practice |
| Capability 4: | Critical Thinking |
| Capability 5: | Solution Seeking |
| Capability 6: | Communication |
| Capability 7: | Collaboration |
Learning Outcomes
- Distinguish between dependent and independent data (Capability 3, 4 and 5)
- Apply the techniques of basic theory of mixed and generalized linear mixed models to construct appropriate working models to answer a specific research question (Capability 3, 4 and 6)
- Apply model building techniques (Capability 3, 4, 5 and 6)
- Apply model selection techniques (Capability 3, 4, 5 and 6)
- Implement an analysis plan when faced with clustered or longitudinal data (Capability 3, 4 and 6)
- Produce results in a way that researchers can understand (Capability 3, 6 and 7)
- Use and interpret the results back to the research question(s) (Capability 3, 4, 5 and 6)
- Demonstrate and apply a good understanding of the statistical software package R and SAS (Capability 3, 4 and 5)
Assessments
| Assessment Type | Percentage | Classification |
|---|---|---|
| Assignments | 100% | Individual Coursework |
| 100% |
| Assessment Type | Learning Outcome Addressed | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |||
| Assignments | ||||||||||
Key Topics
- Random effects models: with both Normally distributed data and non-normally distributed data
- Repeated measures models: with both Normally distributed data and non-normally distributed data
- Random coefficients models: with both Normally distributed data and non-normally distributed data
Workload Expectations
For this course, a typical weekly workload includes:
- 2 hours of lectures
- A 1-hour computer lab
- Additional hours spent reviewing the course content, completing assignments and/or test preparation
Delivery Mode
Campus Experience
Attendance is expected at scheduled activities including labs/tutorials to complete the components of the course.
Lectures will be available as recordings. Other learning activities including tutorials/labs will be available as recordings, but may not be very instructive as these are individual help sessions.
The course will not include live online events including tutorials.
Attendance on campus is required for the exam and for the test, if the test is not an online test.
The activities for the course are scheduled as a condensed weekly timetable.
Learning Resources
Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).
Please remember that the recording of any class on a personal device requires the permission of the instructor.
- Course materials are made available in a learning and collaboration tool called Canvas which also includes lecture recordings
- Coursebooks can be purchased from the Student Resource Centre. Please let the Lecturer know in advance if you wish to purchase one so that it can be ordered
Student Feedback
During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.
At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.
Your feedback helps to improve the course and its delivery for all students.
The course notes have been updated in response to student feedback.
This feedback included:
Peer-review of the course (s2, 2023) which recommended font changes on slides, more use of pointer during lectures and allowing more time ("the elongated awkward pause") to get students to respond to questions.
We incorporated a structured computer-lab session into Friday's 2-hour lecture (as per s2 2023 recommendation) although the timing of the Friday session (3pm-5pm) this year (s2 2024) meant we didn't have many students attending class. Instead, I conducted the tutorial as an online recording (showing SAS code, compiling it, running through the log and its meaning then looking through the results and interpreting those in real-time). Difficult to trouble-shoot problems with students when no students are present (had this for 4-weeks) so discussed potential issues they may have (including running examples of problematic code and showing what it would cause).
Academic Integrity
The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework, tests and examinations as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. A student's assessed work may be reviewed against electronic source material using computerised detection mechanisms. Upon reasonable request, students may be required to provide an electronic version of their work for computerised review.
Class Representatives
Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.
Copyright
The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.
You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.
- Please remember that the recording of any class on a personal device requires the permission of the instructor.
Inclusive Learning
All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.
Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website http://disability.auckland.ac.nz
Special Circumstances
If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.
If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.
This should be done as soon as possible and no later than seven days after the affected test or exam date.
Learning Continuity
In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.
The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.
Student Charter and Responsibilities
The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.
Disclaimer
Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.
In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.