Multilevel Modelling: a robust analytical method for randomised controlled trials (few places remaining)
Date:
27/03/2023 - 29/03/2023
Organised by:
NCRM and DRMC
Presenter:
Professor Jochen Einbeck, Dr Akansha Singh, Dr Bilal Ashraf, Dr Nasima Akhter, and Germaine Uwimpuhwe
Level:
Intermediate (some prior knowledge)
Contact:
Jacqui Thorp
Training and Capacity Building Coordinator, National Centre for Research Methods, University of Southampton
Email: jmh6@soton.ac.uk
Map:
View in Google Maps (DH1 3LE)
Venue:
Scott Logic Lecture Theatre, Mathematics and Computer Science Building, Durham University
Description:
This course will focus on the conceptual understanding of multilevel modelling and its relevance for robust analysis of evidence from randomised controlled trials, with case studies from educational and clinical trials. It will focus on ‘meaning’ and application of multilevel models instead of computations. The course will run for three days with the first day focusing on the transition from linear regression models to multilevel models. Practical examples with simple exercises will be used to motivate the need for a more robust approach than t-tests or linear regressions in randomised controlled trials. The different sources of variability will be discussed as well as their implications on effect size. The course will primarily be taught in R, but we would also be able to support individual exercises in STATA. This is an intermediate course that requires good understanding of the linear regression model as a prerequisite.
The course covers:
- Simple and multiple linear regression
- Overview of multisite and cluster randomised controlled trials
- Hierarchical and correlated data structures
- Random intercepts models
- Random site by intervention models
- Multilevel models for longitudinal data
By the end of the course participants will:
- Gain practical skills in converting data to long form
- Make a link between study design and analytical choice
- Gain practical skills in applying multilevel models and interpreting results
- Acquire necessary skills to check robustness of results from educational or clinical trials
This course is suitable for postgraduate students, researchers, trial statisticians and methodologists and participants should have a basic understanding of statistical methods including the linear regression model and analysis of variance. Participants require access to computer with R enabled software. It is recommended to use RStudio for coding and running R.
Preparatory Reading
Delegates are expected to be broadly familiar with the material in Chapters 1-7 and 10 in
Discovering Statistics Using R by Andy Field, Jeremy Miles and Zoë Field
so it recommended to revise any unfamiliar content prior to the event. No detailed reading of the book is required.
Programme
March 27: 10:30am – 5:30pm (with registration at 10am)
March 28: 10am – 5pm
March 29: 10am – 4:30pm
Cost:
The fee per teaching day is: • £35 per day for students registered at University. • £75 per day for staff at academic institutions, Research Councils researchers, public sector staff and staff at registered charity organisations and recognised research institutions. • £250 per day for all other participants In the event of cancellation by the delegate a full refund of the course fee is available up to two weeks prior to the course. NO refunds are available after this date. If it is no longer possible to run a course due to circumstances beyond its control, NCRM reserves the right to cancel the course at its sole discretion at any time prior to the event. In this event every effort will be made to reschedule the course. If this is not possible or the new date is inconvenient a full refund of the course fee will be given. NCRM shall not be liable for any costs, losses or expenses that may be incurred as a result of its cancellation of a course, including but not limited to any travel or accommodation costs. The University of Southampton’s Online Store T&Cs also continue to apply.
Website and registration:
Region:
North East
Keywords:
Quantitative Software, Randomized Controlled Trials, Educational Interventions, Hierarchical models, Mixed models, Random effects
Related publications and presentations: