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2016 Stata Summer School, London

We are pleased to confirm the dates for the 2016 Stata Summer School, comprising a series of four 1-day courses and one 2-day course running consecutively between 4-9 July 2016 at Cass Business School, London, UK. All courses will be delivered interactively using the internationally used software package Stata.

This is a great opportunity for students, academics and professionals to expand their econometrics skills and learn how they can apply econometrics and statistics from professionals pioneering research at the forefront of their specialist fields.

View courses details, including prices and registration

Course 1: An introduction to Stata

Date: 4 July 2016
Delivered by: Tim Collier, London School of Hygiene & Tropical Medicine

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

Course Outline:

  • Brief overview of Stata’s statistical, graphical and data management capabilities
  • Introduction to the Stata working environment
  • Using Stata via the Graphical User Interface and the command window
  • Understanding Stata’s command syntax
  • Helping you to help yourself – introducing Stata’s online help facilities
  • Working efficiently with do-files
  • Saving results output in a log file

Course 2: An introduction to Stata graphics

Date: 5 July 2016
Delivered by: Tim Collier, LSHTM

This one-day introductory course is intended for people who would like to be able to produce publication-quality graphs using Stata. Some experience of using Stata and some level of statistical knowledge would be helpful though not essential.


Stata enables the production of a wide range of publication-quality graphs. All Stata’s graphics features can be accessed through the Graphical User Interface (GUI) making it simple to produce eye-catching graphs. With Stata’s integrated Graph Editor you can change anything about your graph; you can modify, add or remove titles, lines, text, marker symbols and much more. The Graph Editor features a record and playback facility which enables sets of changes to be saved and then applied to a series of graphs. Stata has a series of built-in graph styles, but it is also possible to create your own style that can easily be applied to any graph. Producing graphs using the command syntax in do-files enables easy reproduction of graphs and can save time when creating similar graphs.

Course outline:

  • Introduction to Stata graphics
  • Resources for learning Stata graphics
  • Producing graphs using the Graphical User Interface
  • Producing graphs using the command syntax in do-files
  • Editing graphs using Stata’s Graph Editor
  • Combining graphs
  • Graph schemes

Course 3: Advanced data management in Stata

Date: 6 July 2016
Delivered by: Tim Collier, LSHTM

This one-day course is intended for people who are reasonably familiar with Stata (e.g. have attended the one-day introduction to Stata course) but who would like to develop their data management skills and work more efficiently.


As well as statistical and graphical capabilities Stata also has an excellent, flexible and wide ranging suite of tools for data management. In this one-day course we will looks at how to handle data of different types, e.g. continuous, categorical, string and dates. We will look at how to change the shape of a dataset e.g. transpose or collapse to create a summary dataset. We will also learn some simple programming tools that will help you save time in your research.

Course outline:

  • Introduction to course and brief review of Stata basics
  • Introduction to how Stata is organised
  • Loading data into Stata from non-Stata formats
  • Useful functions for creating summary variables
  • Dealing with string variables and dates in Stata
  • Changing the shape of your data
  • Creating summary datasets
  • Using Stata’s system variables for data management tasks
  • Some simple programming tools for saving time

Course 4: An introduction to Stata for medical statistics

Date: 7 - 8 July 2016
Delivered by: Tim Collier & Tim Clayton, LSHTM

this introductory medical statistics course is intended for people who are reasonably familiar with Stata (e.g. have attended the one-day introduction to Stata course) but who would like to develop their statistical analysis skills.


In this two-day course we will look at how to use Stata to analyse data that typically arises in medical research including continuous outcomes e.g. blood pressure, binary outcomes e.g. dead/alive and time-to-event outcomes e.g. time to myocardial infarction. We will look at how to fit appropriate models in Stata and how to interpret the resulting output. We will look at how to adjust for multiple explanatory variables, allow for interactions and how to obtain model predictions.

Course Outline:

  • Introduction to course and data
  • Principles of statistical analysis
  • For each of continuous, binary and time-to-event outcomes:
    • Fitting models in Stata and understanding the output
    • Including categorical explanatory variables
    • Fitting multiple explanatory variables
    • Fitting interactions
    • Comparing models and model selection
    • Obtaining model predictions
    • Reporting results
  • For time-to-event outcomes:
    • Setting up survival data in Stata

Course 5: An introduction to meta-analysis

Date: 9 July 2016
Delivered by: Prof. Aurelio Tobías, Spanish Scientific Research Council

A one-day course that is aimed at both academics and practitioners, with a basic knowledge of Stata, who are interested in applying meta-analysis using Stata commands designed for this purpose.


Meta-analysis is a statistical technique for combining the findings from independent studies. This one-day course introduces the main statistical techniques for meta-analysis and shows how to do it in practice using the Stata commands metan, metareg and mvmeta.

Course Outline:

  • Basic Stata commands for meta-analysis
  • Effect sizes based on binary and continuous data
  • Fixed vs. random effects models for meta-analysis
  • Testing for heterogeneity
  • Subgroup analysis and meta-regression
  • Practical exercises using the metan and metareg commands
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