Training Calendar

2019 Stata Summer School, London

Cass Business School 6 days (1st July 2019 - 6th July 2019) Stata Various
Data visualization, Medical statistics, Statistics, Summer School, Various methods

Overview

Our 2019 Stata Summer School comprises a series of 1-day courses, taking place between 1-6 July 2019 at Cass Business School, London. All courses will be delivered interactively using the internationally used software package Stata 15.

Now in its 8th year, our London Stata Summer school provides a very popular and flexible course framework allowing cost-effective attendance at any course separately, or the entire school. 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.

The courses forming the Summer School are:

  • Course 1: An Introduction to Stata - 1 July 2019
  • Course 2: Advanced Data Management in Stata - 2 July 2019
  • Course 3: An Introduction to Stata Graphics - 3 July 2019
  • Course 4: An Introduction to Stata for Medical Statistics - 4 & 5 July 2019
  • Course 5: An Introduction to Meta-Analysis - 6 July 2019

All of the courses forming the Stata Summer School are interactive and includes both a theory review and computer practical sessions using Stata.

Attendees should bring a laptop. A temporary, time limited training license of Stata will be provided to you ahead of the course for you to install. If it is not possible for you to bring your own laptop, or prefer to use one supplied by us, this can be organised by prior arrangement. Additional charges apply.

Learn How to Present your Research Effectively with Timberlake's Poster Sessions

The contents of our summer schools are constantly updated to reflect the most recent trends in research and the evolution of the econometric and statistical software. We appreciate that for most of the participants to our summer schools, research plays an important role in their professional career, but learning how to present research methodology and results and getting feedback from experienced scholars is not always that easy. For this reason, we are introducing in our summer schools extra sessions dedicated to train participants on how to present and discuss their research effectively.

We will organise dedicated sessions during lunch breaks or over drinks where participants willing to receive feedback on their research will present it using a poster. This informal environment will encourage the discussion among the school’s participants and the lecturers.

We request those who are interested in participating into these sessions to create a poster summarising the main elements of their research: motivation, background, methodology, results, conclusion. (If research is at an early stage and no results have been produced yet, the description of the motivation and of the methodology will be enough).

Contact us at training@timberlake.co.uk if you would be interested in participating in a poster session detailing your research. Find out more about this here.

Course Agenda

Course 1: An Introduction to Stata

Date: 1 July 2019
Delivered by: Tim Collier, LSHTM
Learning Ratio:Theory 20% Demonstration 30% Practical 50%
Prerequisites:No prior knowledge of Stata is required.

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

Stata is an integrated statistical package used by researchers from many different disciplines. This course requires no prior knowledge of Stata but assumes an interest in research and in learning to use Stata. This course introduces you to the Stata working environment and to some of the essential data management and statistical tools.

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.

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
An example data analysis project in Stata:
  • Loading data into Stata from Excel
  • Looking at data and checking for and correcting errors
  • Generating new variables
  • Combining data files to create an analysis dataset
  • Good housekeeping – labelling your data
  • Descriptive statistics: tables and figures
  • Hypothesis tests: t-test and chi-square test

The examples used throughout will be from the field of medical statistics. However the underlying principles will have application in many areas of research. Throughout the day we will emphasise good practice for data management and analysis. The format for the training will be a series of ‘from the front’ demonstrations, which participants will follow along on their own computers, interspersed with short exercises.

Learning Objectives:

The key objective of the day is to move you to a position where you are familiar or even friendly with Stata. To be more specific we want you to be familiar with Stata’s various windows and drop-down menus, understand how Stata deals with data and, importantly, know how to write, save and execute commands within do-files. You will learn key data management commands for loading data, investigating data, creating new variables and amending existing variables and combining datasets. You will also learn some commands for obtaining descriptive statistics, including tables and simple graphs, some common hypothesis tests and also linear regression. You will also learn how to use Stata’s online help facilities so that you will be able to continue learning beyond the course.

Agenda

Session 1:

  • Course overview and brief overview of Stata’s capabilities
  • Introduction to the Stata windows
  • Using Stata via the Graphical User Interface

Session 2:

  • Using Stata’s help facilities
  • Working with do-files
  • Loading data and checking for errors

Session 3:

  • Essential data management and housekeeping commands

Session 4:

  • Descriptive statistics: tables and figures
  • Hypothesis tests: t-test and chi-square test
  • Saving results in log-files

Suggested reading:

Course 2: Advanced Data Management in Stata

Date: 2 July 2019
Delivered by: Tim Collier, London School of Hygiene & Tropical Medicine
Learning Ratio: Theory 20% Demonstration 30% Practical 50%
Prerequisites: Familiarity with Stata is required.

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.

Overview:

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

The examples used throughout will be from the field of medical statistics. However, the underlying principles will have application in many areas of research. Throughout the day we will emphasise good practice for data management and analysis. The format for the training will be a series of ‘from the front’ demonstrations, which participants will follow along on their own computers, interspersed with short exercises.

Learning Objectives:

The objective of this 1-day course is to introduce you to Stata’s graphics facilities and give you a flavor of Stata’s graphics potential. Given the broad range of plot types and the huge array of options available (the Stata Graphics Reference manual consists of 600+ pages) it is not possible to teach or demonstrate all of Stata’s graphics facilities in a one-day course. So, firstly, you will be introduced to Stata’s comprehensive help facility and pointed towards other sources of help. This should enable you to build upon what is taught here and to continue learning to create effective graphs beyond the course. By the end of the course you should be able to produce publication quality graphs.

Agenda:

Session 1:

  • Loading data from non-Stata formats
  • Combining datasets
  • Tools for creating derived and summary variables
  • Data management tools for string variables and dates

Session 2:

  • Creating summary datasets
  • Reshaping datasets

Session 3:

  • Working efficiently with macros and loops
  • Saving time with basic programming tools

Suggested reading:

Course 3: An Introduction to Stata Graphics

Date: 3 July 2019
Delivered by: Tim Collier, London School of Hygiene & Tropical Medicine
Learning Ratio: Theory 20% Demonstration 30% Practical 50%
Prerequisites: Some experience of using Stata and some level of statistical knowledge would be helpful though not essential.

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.

Overview:

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
An example graph project:
  • Data management for a graph
  • Building up a graph command
  • Adding titles, legends, text etc.
  • Saving and exporting graphs

Graph types to be covered during the day include: The two-way family of plots e.g. scatter plots and range plots; The Kaplan-Meier survival plot; Distributional plots e.g. including histograms, box-plots, bar charts.

Participants will be able to take away course notes, example datasets used during the day and some example do-files containing graph commands. The format for the training will be a series of ‘from the front’ demonstrations, which participants will follow along on their own computers, interspersed with short exercises.

Learning Objectives:

The key objective is for you to learn the key Stata’s commands for carrying out advanced data management and processing. By the end of this course you should understand how Stata is organized and be able to use simple programming tools to save time and to carry out non-standard data management tasks.

Course Overview

This course provides a thorough overview of how Stata is organised and good practice for data management.

Agenda:

Session 1:

  • Course overview and flavour of Stata graphics capabilities
  • Resources available for Stata graphics
  • Producing graphs using the Graphical User Interface

Session 2:

  • Producing graphs using the command syntax and do-files
  • Adding and modifying titles, labels, legends, text and more

Session 3:

  • Saving and exporting graphs
  • Editing graphs in the graph editor
  • Combining graphs
  • Graph schemes

Session 4:

  • Understanding the anatomy of a Stata graph
  • Graph project: select a graph example project and build graph in do-file, from data processing to saving for publication

Suggested reading:

Course 4: An Introduction to Stata for Medical Statistics

Date: 4 & 5 July 2019
Delivered by: Tim Collier & Tim Clayton, London School of Hygiene & Tropical Medicine
Learning Ratio: Theory 20% Demonstration 30% Practical 50%
Prerequisites: Familiarity with Stata is required. Basic knowledge of statistics and econometric concepts such as linear regression, logistic regression and survival analysis is advisable.

Overview:

This two day introductory 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

An example study will be used throughout to help illustrate how to carry out a statistical analysis.

The examples used throughout will be from the field of medical statistics. However, the underlying principles will have application in many areas of research. Throughout the day we will emphasise good practice for carrying out and reporting statistical analysis. The format for the training will be a series of ‘from the front’ demonstrations, which participants will follow along on their own computers, interspersed with short exercises. A series of do-files will be built up over the two days which participants can take away along with the data as a record of what they have done.

Learning Objectives:

The two key objectives are that by the end this course you will know how to fit statistical models in Stata for a variety of outcomes and know how to interpret the output from these models. You should be able to compare models to select the most appropriate model and be able to report the results effectively.

Agenda:

DAY 1

Session 1: Linear Regression 1

  • Analysis of continuous outcome variables
  • Univariable linear regression
  • Fitting binary, categorical and continuous explanatory variables

Session 2: Linear Regression 2

  • Multivariable linear regression
  • Fitting quadratic and interaction terms
  • Comparing models using F-tests
  • Obtaining fitted values and model checking

Session 3: Logistic Regression 1

  • Analysis of binary outcomes
  • Univariable logistic regression
  • Fitting binary, categorical and continuous explanatory variables

Session 4: Logistic Regression 2

  • Multivariable logistic regression
  • Comparing models using likelihood ratio tests
  • Assessing model performance using area under the curve

DAY 2

Session 1: Survival Analysis 1

  • Introduction to analysis of time-to-event outcomes
  • Setting up data for survival analysis
  • Commands for summarising survival data

Session 2: Survival Analysis 2

  • Producing Kaplan-Meier curves
  • Univariable Cox proportional hazards modelling
  • Fitting binary, categorical and continuous explanatory variables

Session 3: Survival Analysis 3

  • Multivariable Cox proportional hazards modelling
  • Comparing models using likelihood ratio tests
  • Testing the proportional hazards assumption

Session 4: Q&A session

  • Multivariable logistic regression
  • Comparing models using likelihood ratio tests
  • Assessing model performance using area under the curve

Suggested reading:

  • An Introduction to Stata for Health Researchers - Svend Juul
  • A Handbook of Statistical Analysis Using Stata 4th edition - Sophia Rabe-Hesketh and Brian Everitt
  • Regression Methods in Biostatistics – Linear, Logisitic, Survival and Repeated Measures Models–Eric Vittinghoff, David Glidden, Stephen Shiboski, Cahrles McCulloch, Springer

The following books are good general introductions to medical statistics but do not focus on any statistical software:

  • Essential Medical Statistics - B Kirkwood and J Sterne, 2nd Edition, Blackwell Science
  • Practical Statistics for Medical Research – Doug Altman, Chapman & Hall/CRC

Course 5: An Introduction to Meta-Analysis

Date: 6 July 2019
Delivered by: Prof. Aurelio Tobías, Spanish Scientific Research Council
Learning ratio: 30% theory, 20% demonstration and 50% practical

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.

Overview:

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 metanmetareg 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

Session 1:

  • Basic Stata commands for meta-analysis
  • Effect sizes based on binary and continuous data

Session 2:

  • Fixed vs. random effects models for meta-analysis
  • Testing for heterogeneity

Session 3:

  • Subgroup analysis and meta-regression

Session 4:

  • Practical exercises using the metan and metareg commands
TimeSession / Description
08:45-09:15 Arrival & Registration
Arrival and Registration from 9am for Courses 2-6.
09:30-11:00 Session 1
11:00-11:15 Tea/coffee break
11:15-12:45 Session 2
12:45-13:45 Lunch
13:45-15:15 Session 3
15:15-15:30 Tea/coffee break
15:30-17:00 Session 4

Prerequisites

Course 1: An Introduction to Stata

  • No prior knowledge of Stata is required.

Course 2: Advanced Data Management in Stata

  • Familiarity with Stata is required.

Course 3: An Introduction to Stata Graphics

  • Some experience of using Stata and some level of statistical knowledge would be helpful though not essential.

Course 4: An Introduction to Stata for Medical Statistics

  • Familiarity with Stata is required. Basic knowledge of statistics and econometric concepts such as linear regression, logistic regression and survival analysis is advisable.

Course 5: An Introduction to Meta-Analysis

  • Basic knowledge of Stata and systematic reviews.

Terms & Conditions

  • Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
  • Additional discounts are available for multiple registrations.
  • Cost includes course materials, lunch and refreshments.
  • Delegates are provided with temporary licences for the principal software package(s) used in the delivery of the course. It is essential that these temporary training licenses are installed on your computers prior to the start of the course. We can provide laptops to attendees. Prior notice is required and additional charges will apply.
  • Should you need assistance in locating hotel accommodation in the immediate vicinity of the course location, please notify us at the time of booking.
  • Payment of course fees required prior to the course start date.
  • Registration closes 5-calendar days prior to the start of the course.
    • 100% fee returned for cancellations made more than 28-calendar days prior to start of the course.
    • 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
    • No fee returned for cancellations made less than 14-calendar days prior to the start of the course.

The number of attendees is restricted. Please register early to guarantee your place.

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