Module manager: Duncan Wilson
Email: D.T.Wilson@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2024/25
Pre-requisite for this module is either MATH2715 OR MATH2735 - you do not need to have studied both.
| MATH2715 | Statistical Methods |
| MATH2735 | Statistical Modelling |
| MATH5092M | Mixed Models with Medical Applications |
This module is not approved as an Elective
Clustering and hierarchical structures are common in scientific research. For example, in a medical study, patients could be clustered by surgeon, with surgeons clustered by hospital. Patient outcomes within clusters are likely to be correlated, thus violating the independence assumptions of many statistical methods. Mixed models overcome this obstacle, by extending familiar models such as linear regression to include additional random terms that account for variability between clusters. Mixed models are essential tools in the medical, environmental and social sciences. Students undertaking this module will learn how to apply mixed models to a broad range of realistic examples from these sciences.
This module aims to introduce students to mixed models for the analysis of data with complex hierarchical or clustered structures. These models will be demonstrated through real-life examples from the medical, environmental and social sciences.
By the end of this module, the student should be able to:
1. Understand and explain mixed models and how they are estimated.
2. Apply appropriate mixed models to problems in the medical, environmental or social sciences.
3. Estimate a variety of mixed models using statistical software.
4. Report and interpret the results of a mixed model analysis.
1.Basic two-level hierarchical models, including variance components models, random intercept models and random slope models.
2.Three-level hierarchical models and complex variance structures.
3.Parameter and residual estimation for mixed models.
4.Mixed models for longitudinal data.
5.Fitting and checking models using statistical software.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 11 | 1 | 11 |
| Practical | 11 | 1 | 11 |
| Independent online learning hours | 22 | ||
| Private study hours | 56 | ||
| Total Contact hours | 22 | ||
| Total hours (100hr per 10 credits) | 100 | ||
Each week, to prepare for the computer practicals, students will be expected to independently spend two hours reading through a set of online notes and completing exercises on material not introduced in the lectures. Students should arrive at the practicals fully prepared to discuss this material.
Students will complete and receive written feedback on three homework assignments. Students will receive face-to-face feedback on tasks completed during practical sessions.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Report | A short report (< 10 pages) on an analysis of a data set. | 20 |
| Total percentage (Assessment Coursework) | 20 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Standard exam (closed essays, MCQs etc) | 2.0 Hrs Mins | 80 |
| Total percentage (Assessment Exams) | 80 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
The reading list is available from the Library website
Last updated: 4/29/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team