Introduction to Linear and Logistic Regression Models
This course aims to provide an understanding of the statistical principles behind, and the practical application of, univariable and multivariable linear and logistic regression in medical, epidemiological and health services research.
|Date||6 - 10 March 2023|
|Structure||Taught over 5 consecutive full days|
|Audience||Open to all applicants|
|Course Organisers||Dr Stephanie MacNeill & Dr Emily Kawabata|
Full course details
This 5-day course will be online and consist of a mix of live and pre-recorded sessions and practical work. Over the five days there will be approximately 14 hours of lectures and 15 hours of practicals.
By the end of the course participants should be able to:
- have a thorough conceptual understanding of linear and logistic regression;
- appreciate the common threads running through these methods, including stratified analysis, different options for handling explanatory variables, and concepts such as confounding and interaction;
- have a working knowledge of the Stata commands to run these models, and a thorough understanding of the output generated from such a package; and
- know the basis on which analytical strategy and model choice is made, and how the results should be interpreted.
Who the course is intended for
The course is intended for people analysing data from medical, epidemiological, and health services research, who have used simple methods such as t-tests and chi-square tests, and who now wish to use multivariable methods to control confounding, accommodate interaction, and increase statistical power.
The course will cover:
- revision of basic methods in a statistical modelling framework;
- terminology in regression modelling;
- indicator variables to incorporate categorical explanatory variables;
- univariable and multivariable regression;
- common features of linear and logistic regression models;
- interpretation of model coefficients as differences in means or odds ratios;
- adjustment for confounding and exploring effect modification (interaction) in multivariable regression;
- comparing models with Wald tests and likelihood ratio tests;
- model assumptions; and
- analytical strategies.
IMPORTANT PREREQUISITES - please read before booking
Please ensure you meet the following prerequisites before booking:
|Knowledge||You should have knowledge of statistical methods and their implementation in Stata of at least the level achieved in the Introduction to Statistics short course.|
|Software||You must have Stata (version 15 or higher)* installed in advance of the course.
*Internal University of Bristol participants are given access to Stata. Go to Stata Installation Instructions (internal only) for help setting it up before the start of the course. External participants are responsible for providing their own access to Stata.
This course is part of our core programme
Postgraduate Research Students in the Bristol Medical School receive advance access to the materials for this course at the start of each academic year. Find out more about MO Resources for PhD Students.
Bookings for this course have now closed
Dates don't work? Just need a refresher?
Find out about the self-paced 'Materials Only' version of this course [available to University of Bristol staff and research postgraduates only].