- Department: Health Sciences
- Module co-ordinator: Prof. Mona Kanaan
- Credit value: 10 credits
- Credit level: M
- Academic year of delivery: 2022-23
- See module specification for other years: 2021-22
Pre-requisite modules
Co-requisite modules
- None
Prohibited combinations
- None
Occurrence | Teaching period |
---|---|
A | Autumn Term 2022-23 |
To equip students with the necessary skills and knowledge to allow analysis of multilevel data. By means of lectures and hands-on analysis of data from real studies using the statistical software package STATA. The student is guided through a range of statistical techniques that can be used based on the nature of the outcome when the data follow a multilevel structure.
At the end of the module, the student should be able to:
Academic and graduate skills
Students will be able to carry out multilevel data analysis on a number of outcomes using a statistical package and critically read research papers that use multilevel analysis.
Prior to commencing the module, students are expected to demonstrate knowledge of ordinary linear, logistic, and survival regression methods and ability to carry these regressions.
Subject content Revision of Generalised Linear Models Topics to include multiple linear regression, logistic regression, Poisson regression, and Survival Analysis
What is Multilevel modelling? Introduce a range of multilevel structures, e.g nesting and cross classification, with examples from real studies, introduce how to represent multilevel structures using subscripts, distinguish between levels and variables, and fixed and random effects.
Multilevel modelling for a continuous outcome Introduce the random intercept and the random slope models pointing out why standard linear regression does not work in the case of multilevel structures together with the assumptions underlying these models and sample size issues.
Multilevel modelling for a binary outcome and count data Extend the random intercept and the random slope models to the case of a binary response and count data focusing on how to interpret the models and introduce the latent variable approach in the case of binary outcome.
Multilevel modelling for time-to-event data Introduce the discrete time approach to analyse time-to-event data and extend the model to the multilevel case.
Multilevel modelling in cluster randomised trials A brief introduction to cluster randomised trials and how to analyse them using multilevel modelling also hinting to other methods used to analyse cluster randomised trials. |
Task | Length | % of module mark |
---|---|---|
Online Exam - 24 hrs (Centrally scheduled) Introduction to Applied Multilevel Analysis |
N/A | 100 |
None
Task | Length | % of module mark |
---|---|---|
Online Exam - 24 hrs (Centrally scheduled) Introduction to Applied Multilevel Analysis |
N/A | 100 |
Students are provided with collective exam feedback relating to their cohort, within the timescale specified in the programme handbook.