Training Calendar

2020 Stata Summer School: ONLINE

Online 5 days (13th July 2020 - 17th July 2020) Stata Various
Data visualization, Medical statistics, Statistics, Summer School, Various methods

Overview

The Stata Summer School consists of a series of one- and two-day courses which can be taken individually or as a whole as required. The School is aimed at students, academics and professionals who want to develop and strengthen their data processing, programming, graphics and statistical skills using Stata. All of the courses are taught interactively using a blend of theory, follow-along demonstrations and exercises.

The Stata Summer School will be running online, via Zoom, this year.

The courses forming the Summer School are:

  • Course 1: An Introduction to Stata - 13 July 2020
  • Course 2: Advanced Data Management in Stata - 14 July 2020
  • Course 3: An Introduction to Stata Graphics - 15 July 2020
  • Course 4: An Introduction to Stata for Medical Statistics - 16 & 17 July 2020

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

A temporary, time limited training license of Stata will be provided to you ahead of the course for you to install.

Course Agenda

*Please note that the agenda may change slightly.

Course 1: An Introduction to Stata

Date: 13 July 2020
Delivered by: Tim Collier, LSHTM
Learning Ratio:Theory 20% Demonstration 30% Practical 50%
Prerequisites:No prior knowledge of Stata is required.

An Introduction to Stata is a one-day introductory course for people interested in learning to using Stata for research. This course requires no prior knowledge of Stata but assumes an interest in research and in learning to use Stata efficiently. This course introduces you to the Stata working environment, introduces the two main ways of working in Stata, and to some of the essential tools for data management. 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. 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 course.

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.
  • Two ways of working in Stata: the Graphical User Interface and do-files.
  • Understanding Stata’s command syntax.
  • Importing data from Excel.
  • Getting to know your data.
  • Generating new variables.
  • Housekeeping for tidy data.
  • Saving results in a log file.

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.

Suggested reading:

Course 2: Data Management in Stata

Date: 14 July 2020
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:

Data Management using Stata is a one-day course intended for people who have some prior experience with Stata but who would like to develop their data management skills and be able to work more efficiently. This course covers importing data from Excel, combining datasets, and tools for dealing with numeric variables, string variables and dates. The course also covers some basic programming tools, including loops, to improve efficiency with repetitive tasks. We will focus mainly on building up commands in do-files.

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. 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 course.

Course outline:

  • Importing data from Excel
  • Combining datasets - merging and appending
  • Commands for creating new variables
  • Dealing with string variables
  • Working with dates in Stata
  • Creating summary data sets
  • Working efficiently with loops

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.

Suggested reading:

Course 3: Data Visualisation using Stata

Date: 15 July 2020
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:

Data Visualisation using Stata is a one-day course intended for people who would like to be able to produce publication-quality graphs using Stata. During the course we will create a number of different graph types using the Graphical User Interface. We will start with a simple example and build in complexity. Many of the options available for creating clear, effective, publication-ready graphs will be demonstrated. We will learn how to save the command syntax in a do-file to enable the graph to be easily reproduced and edited if required. We will also cover graph schemes and exporting graphs in different formats.

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 visualisation. 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 course.

Course outline:

  • Introduction to Stata graphics and useful resources.
  • Creating graphs through the Graphical User Interface.
  • Saving graph commands in do-files for efficient reproduction.
  • Graph options – making a graph ready for publication.
  • Saving graphs for presentations.
  • Exporting graphs in different formats.
  • Good practice for creating effective graphs.

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.

Suggested reading:

Course 4: An Introduction to Stata for Medical Statistics

Date: 16 & 17 July 2020
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:

Stata for Medical Statistics is a two-day course 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 arise in medical research. We will cover linear regression for continuous, logistic regression for binary outcomes and poison and Cox regression for time-to-event outcomes. We will look at how to produce summary statistics to help you understand the data and summarise variables. We will cover how to select and fit appropriate models in Stata and, as importantly, how to interpret the resulting output. We will look at how to adjust for multiple explanatory variables, compare models, investigate assumptions, and how to plot predicted values. For survival analysis 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 in many areas of research. Throughout the day we will emphasise good practice for data visualisation. 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 each day, but also opportunities to ask questions throughout the course.

Course Outline:

  • Day 1: Linear Regression and Logistic Regression
  • Day 2: Survival analysis using Poison and Cox Regression
  • For each of continuous, binary and time-to-event outcomes:
  • 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.
  • Obtaining predictions and plotting fitted values.
  • Reporting results.
  • Kaplan-Meier plots for time to event outcomes
  • 30 minutes Q&A at the end of each day

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.

Suggested reading:

  • An Introduction to Stata for Health Researchers - Svend Juul
  • Essential Medical Statistics - B Kirkwood and J Sterne, 2nd Edition, Blackwell Science

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.

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.
  • 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.
  • Payment of course fees required prior to the course start date.
  • Registration closes 1 calendar day 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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    1-day pass (13/07/2020 - 17/07/2020)
    2-day pass (13/07/2020 - 17/07/2020)
    3-day pass (13/07/2020 - 17/07/2020)
    4-day pass (13/07/2020 - 17/07/2020)
    5-day pass (13/07/2020 - 17/07/2020)

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