STATS 768 : Longitudinal Data Analysis


2024 Semester Two (1245) (15 POINTS)

Course Prescription

Exploration and regression modelling of longitudinal and clustered data, especially in the health sciences: mixed models, marginal models, dropout, causal inference.

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. 

Course Requirements

Prerequisite: 15 points from ENGSCI 314, STATS 201, 207, 208, 210, 707

Capabilities Developed in this Course

Capability 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 5: Solution Seeking
Capability 6: Communication
Capability 7: Collaboration
Graduate Profile: Master of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Distinguish between dependent and independent data (Capability 3 and 4)
  2. 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 5)
  3. Apply model building techniques (Capability 3, 4 and 5)
  4. Apply model selection techniques (Capability 3, 4 and 5)
  5. Implement an analysis plan when faced with clustered or longitudinal data (Capability 3, 4, 5 and 6)
  6. Produce results in a way that researchers can understand (Capability 3, 4, 5, 6 and 7)
  7. Use and interpret the results back to the research question(s) (Capability 5, 6 and 7)
  8. Demonstrate and apply a good understanding of the statistical software package R and SAS (Capability 3, 4 and 5)


Assessment Type Percentage Classification
Assignments 100% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7 8


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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

This condensed course is a standard 15-point course and students are expected to spend 10 hours for each 1-point course that they are enrolled in.
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:
  • 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.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework 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. This requirement also applies to sources on the internet. A student's assessed work may be reviewed for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

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.


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

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

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 course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, 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


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 students may be asked to submit 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. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 31/10/2023 10:54 a.m.