Other training courses

Learn how to implement parametric survival models, use flexible parametric survival models, and the concept of competing risks in modelling time-to-event data.

Introduction to methods for analysing longitudinal/repeated continuous data, with a focus on methods for analysing individual trajectories over time.

Learn to define causation in biomedical research, describe methods to make causal inferences in epidemiology and health services research, and demonstrate the practical application of these methods.

Understand the statistical principles behind, and the application of, univariable and multivariable linear and logistic regression in medical, epidemiological and health services research.

Provides a grounding in the theory behind the methods most commonly used to analyse rates and survival-time data, as well as extensive hands-on experience of their application in Stata software.

Introduction to the basic concepts and methods commonly used in medical and public health research.

Learn about the underpinning logical arguments for statistical inference and the terminology used in this hands-on course. SPSS is used throughout the day, so attendees are expected to be familiar with this statistical software as well as descriptive statistics.

This course is a logical continuation of the Introduction to Statistics 1 course. Besides continuing to cover the commonly used statistical tests, it also provides an overview of selecting the most appropriate statistical test.

Gain an overview of what Structural Equation Modelling (SEM) is and how it relates to regression techniques and factor analysis. The software AMOS is used and you will develop skills in the use of AMOS, path diagram construction, analysis formats, output management and modelling practice. The course will then build on these concepts and skills to introduce the structural equation modelling.