2025 Stata Summer School

Date23 June 2025
Delivered byTim Collier, LSHTM
Prerequisitesnone

This one-day introductory course is for people interested in using Stata effectively in their research. No prior working knowledge of Stata is required.

Throughout the course there will be an emphasis on good practice for research, understanding work flow, good data management, efficiency and reproducibility of results.

The course will start by introducing the key Stata windows. We will demonstrate how to use Stata interactively via the Graphical User Interface but more time will be spent on working efficiently in do-files. We will briefly introduce Stata’s built-in help files and pdf documentation. 

The most important stage before undertaking detailed data analysis is getting to know your data and identifying and dealing with errors. We will introduce a small toolkit of essential commands for exploratory data analysis. These will include statistical and visual summaries designed to help you understand the variables that you have, how they are distributed, and to identify spurious values and data anomalies. We will show how to save the results of these exploratory analyses in log-files. 

We then cover some of the essential tools for data management including importing data from different formats, generating new variables, correcting errors, keeping data tidy and saving data. We will also introduce two key commands for combining datasets. 

The examples used throughout will be from the field of medical statistics. However, the underlying principles will have application across all areas of research. Participants will be able to take away a comprehensive set of course notes and data used on the course as well as files created throughout the day. There will be a Q&A session at the end of the course, but also opportunities to ask questions throughout the day.

Course Outline

We will cover:

• Introduction to the key Stata windows
• Working interactively in Stata via the Graphical User Interface
• Exploratory data analysis: getting to know your data and identifying errors using statistical and graphical summaries
• Saving commands and results
• Essential data management tools: importing and saving data, generating new variables, correcting errors, keeping data tidy

Learning Objectives

By the end of this course you will:

• be familiar with the main Stata windows;
• understand how to work in Stata via the Graphical User Interface and do-files and the advantages and disadvantages of the two approaches;
• be able to use a small toolkit of commands to carry out exploratory data analyses;
• be able to create and save Stata datasets;
• be able to create new variables and correct errors;
• understand how Stata handles data and the importance of good practice for data management;
• know how to use Stata’s online help facilities so that you will be able to continue learning beyond the course

Date24 June 2025
Delivered byTim Collier, LSHTM
Prerequisitesnone

This one-day introductory course is for people who would like to be able to use Stata to produce clear, effective, publication-quality graphs using Stata. No prior working knowledge of Stata is required. 

Throughout the course there will be an emphasis on good practice for data visualisation, planning before producing graphs, data management for graphics, working efficiently and reproducibility of graphs. 

During the day we will use the Graphical User Interface (GUI) to create a number of different types of graph e.g. box plots, two-way plots, bar charts, etc. We will start with a simple example and build up in complexity. We will cover the key options for creating a publication-ready graph including adding axis titles, adding and amending legends, specifying the colour, size and type of markers or lines that are used, and much more. We will also cover exporting graphs in different formats e.g. as pdf, jpg, emf, for publications or presentations. 

Having produced a graph using the GUI you will then learn how to save the command in a do-file so that the graph can easily be reproduced and amended or even recycled and used to produce new graphs. Graph commands, with all their options, can become very complex and long, so we will demonstrate how to allow a command to flow over many lines and laid out for easier editing. 

The more complex graphs will require some prior data management. We will cover how to create summary datasets containing the results that we need for the graphs. 

The examples used throughout will be from the field of medical statistics. However, the underlying principles will have application across all areas of research. Participants will be able to take away a comprehensive set of course notes and data used on the course as well as files created throughout the day. There will be a Q&A session at the end of the course, but also opportunities to ask questions throughout the day. 

Course Outline

We will cover:

• Producing different types of graphs using the Graphical User Interface
• Options for producing clear, publication-ready graphs
• Exporting graphs in different formats for publications or presentations
• Saving graph commands in do-files for future use and recycling
• Data processing for data visualisation
• Good practice for data visualisation

Learning Objectives

By the end of this course you will:

• understand the importance of planning before producing graphs;
• know how to produce graphs using the Graphical User Interface;
• know how to use graph options to produce a publication ready graph;
• be able to export graphs in a number of different formats;
• be able to copy and paste graphs into a power point presentation;
• be able to create summary datasets for data visualisation;
• be able know how to use Stata’s online help facilities so that you will be able to continue learning beyond the course.

Date25-26 June 2025
Delivered byTim Collier, LSHTM & Tim Clayton. LSHTM
PrerequisitesSome familiarity with Stata is desirable

Stata for Medical Statistics is a two-day course intended for people who have basic level experience with Stata, e.g. have attended the one-day introduction to Stata course, and who would like to develop their statistical analysis skills.  

Throughout the course there will be an emphasis on good practice for data analysis, the workflow of research, and reproducibility of results. There will be a focus on understanding your data, selecting appropriate summary statistics and statistical methods for different types of outcome variables, and on the interpretation and presentation of results. 

Over the two-days we will look at how to use Stata to analyse data that typically arise in medical research. We will cover linear regression for continuous outcomes (day 1), logistic regression for binary outcomes (day 1) and Poisson and Cox regression for time-to-event outcomes (day 2).  

For each of these three types of outcome we will cover exploratory and summary statistics and simple plots, to help you understand the data and summarise variables appropriately. We will also cover simple hypothesis tests.

We will cover how to select and fit appropriate models in Stata, how to adjust for different types of explanatory variables, and understanding and checking model assumptions. 

Importantly, we will spend time focusing on understanding and interpreting the output from each statistical model e.g. effect estimates and confidence intervals. We will consider which of the many results that Stata produces should be selected for presentation and how these results could be presented effectively in tables. 

For time-to-event outcomes we will see how to produce publication quality Kaplan-Meier plots. We will also spend some time thinking about how to report results effectively.

The examples used throughout will be from the field of medical statistics. However, the underlying principles will have application across all areas of research. Participants will be able to take away a set of course notes and data used on the course as well as files created throughout the day. There will be a Q&A session at the end of the course, but also opportunities to ask questions throughout the two days.

Course Outline

We will cover:

• Day 1: Linear and logistic regression models
• Day 2: Poisson and Cox regression models 

For each of these types of regression models we will cover:

• Summary statistics and simple hypothesis tests.
• Selecting the appropriate regression model.
• Fitting models in Stata and understanding the output.
• Including continuous and categorical explanatory variables.
• Selecting and interpreting multivariable models.
• Checking model assumptions.
• Interpreting and reporting results.

Learning Objectives

By the end of this course you will:

• understand the importance of good practice for data analysis;
• be able to distinguish different types of outcomes and select appropriate statistical models;
• be able to fit linear, logistic, Poisson and Cox models and understand and interpret the results;
• be able to produce appropriate figures and tables for displaying results effectively;
• be able to check model assumptions. 

• Comparing estimators for static panel models for your research question 
• Testing for serial correlation

• The Arello Bond estimator and post-estimation diagnostic test 
• The Blundell Bond estimator and post estimation diagnostic tests
• Case study: the determinants of bank risk-taking in European banks.