2021/22 Taught Postgraduate Module Catalogue
MATH5092M Mixed Models with Medical Applications
15 creditsClass Size: 30
Module manager: Dr. Benjamin Thorpe
Email: B.Thorpe@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2021/22
Pre-requisite qualifications
MATH2715 or MATH2735This module is mutually exclusive with
| MATH5091M |
This module is not approved as an Elective
Module summary
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. With a focus on advanced medical applications, students will also learn how clustering can be incorporated into the design of experiments and how to properly analyse data with non-hierarchical clustering.Objectives
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. More advanced models for clustered data with non-hierarchical structures, such as cross-classification and multiple membership, will be introduced through their applications in medical statistics. The optimal design of experiments with clustered data will be covered, with reference to clinical trials.Learning outcomes
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.
5.Understand, explain and estimate mixed models that allow cross-classification and multiple membership.
6.Understand the implications of clustered data on experimental design, and apply appropriate methods for sample size determination in clinical trials with clustering.
Syllabus
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.
6.Design of experiments with clustered data.
7.Models for multiple membership and cross-classification.
Teaching methods
| Delivery type | Number | Length hours | Student hours |
| Lecture | 11 | 1.00 | 11.00 |
| Practical | 13 | 1.00 | 13.00 |
| Independent online learning hours | 22.00 | ||
| Private study hours | 104.00 | ||
| Total Contact hours | 24.00 | ||
| Total hours (100hr per 10 credits) | 150.00 | ||
Opportunities for Formative Feedback
Students will complete and receive written feedback on three homework assignments. Students will receive face-to-face feedback on tasks completed during practical sessions.Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| Report | A short report (< 10 pages) on an analysis of a data set. | 20.00 |
| Total percentage (Assessment Coursework) | 20.00 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Exams
| Exam type | Exam duration | % of formal assessment |
| Open Book exam | 2 hr 30 mins | 80.00 |
| Total percentage (Assessment Exams) | 80.00 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Reading list
The reading list is available from the Library websiteLast updated: 29/03/2022 15:20:27
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