Training Courses

Presented By: Dr. Austin Nichols

This course is for professionals and researchers from all academic disciplines who wish to improve their use of Stata.

Participants will be introduced to Stata and causal inference using practical examples. The fundamentals of data analysis and visualization will also be taught, using appropriate Stata commands and programming techniques

Presented by Sandro Leidi & James Gallagher

This course is running online, via Zoom.

Mixed models have become increasingly popular, as they have many practical applications. However, the traditional linear mixed model with normally distributed errors may not always be appropriate for modelling discrete response variables, such as binary data and counts. Typically these types of responses are analysed using generalised linear models such as logistic regression and Poisson regression.

Commonly-used generalised linear models will be extended to deal with multiple error structures, using a variety of examples, generally drawn from medical and health related fields. Specific applications, such as repeated measurements and multi-centre trials will also be considered. For example, investigating the presence or absence of adverse events collected in a multi-centre clinical trial.

The emphasis will be on practical understanding, although an outline of the theory will be presented. Practical examples will be used to illustrate the methods, and participants will have the opportunity to fit and interpret models themselves in hands-on computer based practicals.

Note this course does not cover marginal or GEE type models, but will consider population-averaged effect measures derived from a generalised linear mixed model.

Overview

Presented by: Dr. Vincent O'Sullivan

This course is for professionals and researchers who are new to Stata. The course assumes only limited statistical knowledge and experience of using statistical software. The participants will be introduced to Stata’s interface. They will be shown how manage and prepare datasets for analysis. The fundamentals of data analysis and visualization will also be taught. Then, the participants will be introduced to two of the main data analysis tools: linear regression and logistic regression. Participants will be taught the statistical theory behind these methods, and they will apply these methods to specially chosen datasets using examples from health research.

Presented by Dr. Giovanni Cerulli

This course will focus on three specific techniques: regression and classification trees (including bagging, random forests, and boosting), kernel-based regression, and global methods (step-wise, polynomial, spline, and series regressions).The teaching approach will be mainly based on the graphical language and intuition more so than on algebra. The training will make use of instructional as well as real-world examples, and will evenly balance theory and practical sessions.

The course is open to people coming from all scientific fields, but it is particularly targeted to researchers working in the medical, epidemiological and socio-economic sciences.

Course Overview

Presented by Dr. Giovanni Cerulli

This course provides participants with the essential tools, both theoretical and applied, for a proper use of instrumental variables (IV) and structural equation models (SEM) for statistical causal modelling using Stata.

The aim of this course is to provide participants with an in-depth understanding of how a good MSc dissertation should look, and how to easily use Stata to obtain any required econometrics.

Participants will receive a free temporary Stata license for the duration of the course

The course is meant for any MSc student writing their MSc dissertation, who needs guidance on the best structure, and most suitable econometric methods to apply. No previous knowledge of Stata is required.

Presented by Dr. Giovanni Cerulli

This course is a primer to machine learning techniques using Stata. Stata owns today various packages to perform machine learning which are however poorly known to many Stata users. This course fills this gap by making participants familiar with (and knowledgeable of) Stata potential to draw knowledge and value from rows of large, and possibly noisy data. The teaching approach will be based on the graphical language and intuition more than on algebra. The training will make use of instructional as well as real-world examples, and will balance evenly theory and practical sessions.

Presented by Dr. Giovanni Cerulli

This course will focus on three specific techniques: regression and classification trees (including bagging, random forests, and boosting), kernel-based regression, and global methods (step-wise, polynomial, spline, and series regressions).The teaching approach will be mainly based on the graphical language and intuition more so than on algebra. The training will make use of instructional as well as real-world examples, and will evenly balance theory and practical sessions.

The course is open to people coming from all scientific fields, but it is particularly targeted to researchers working in the medical, epidemiological and socio-economic sciences.

Presented by Dr. Anis Samet

This workshop is an introduction to regression analysis with categorical dependent variables using the Stata software. It will cover the most commonly used regression models for categorical outcomes: binary logit and probit, ordinal logit, and multinomial logit.

The course assumes that attendees have prior knowledge of common commands in Stata to organize and handle data and undertake standard regression techniques. Nevertheless, this is an introductory course.