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Introduction to Bayesian Methods in Clinical Research [ESP68]

Course highlights

EC points

1.4

Start date

16 August 2021

End date

20 August 2021

Course days

Monday to Friday (5 full days)

Course time

From 8:45 till 16:00 CEST

Faculty

Prof. Emmanuel Lesaffre

Course fee

€ 1250

Location

Online

Level

Advanced

Prerequisites

The participants should have:

  • A good statistical knowledge and practical experience with regression models: linear, binary and survival regression
  • Good to extensive practical experience with programming in R

It is recommended that participants have knowledge and experience with mixed models.


Disciplines

  • Biostatistics
  • Methodology

Course Materials

Online, download instructions will be sent before the start of the course, by e-mail.

A laptop is required. R, WinBUGS and OpenBUGS will be used as software. Please note that WinBUGS and OpenBUGS do not operate with certain versions of Mac. Check their websites for more information.


Recommended book:

Bayesian Biostatistics

E. Lesaffre and A. Lawson

John Wiley & Sons, New York, 2012

ISBN: 978-0-470-01823-1

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Detailed information about this course:

Description

Faculty: Prof. Emmanuel Lesaffre, PhD

The Bayesian approach is an important alternative to the classical (called frequentist) approach to statistics. Indeed, the Bayesian approach has become increasingly important over the last three decades and is invading in all application areas. Especially with complex data the Bayesian approach has proven to be a very useful analysis tool, but also conceptually this approach is attracting recently many researchers.

The course introduces the participant to Bayesian methods for the analysis of clinical and epidemiological studies. While some math cannot be avoided, the emphasis in the course is on bringing over the intuitive ideas and the analysis of clinical and epidemiological data sets using Bayesian software. The course treats the basic concepts of the Bayesian approach, such as the prior and posterior distribution and their summary measures, the posterior predictive distribution. In addition, Bayesian methods for model selection and model evaluation will be treated. Markov Chain Monte Carlo techniques are introduced and exemplified. A great variety of clinical and epidemiological examples illustrates the techniques.

Medical publications are explored in discussion groups, as well as Bayesian analyses of real data sets will be exercised on an individual and on a group basis.


//Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

Objectives

  • Understand the Bayesian concepts
  • Understand clinical and epidemiological papers that make use of the Bayesian approach.
  • Appreciate the impact of the Bayesian approach on clinical and epidemiological research
  • Write an OpenBUGS program and make use of R2OpenBUGS for some basic statistical models

Participant profile

The participant profile includes any of the following type of researchers: clinical researchers, clinical epidemiologists, decision scientists, public health researchers, those in health technology assessment or value-based healthcare, but with the restriction that they should have a sound (see above) statistical knowledge and experience.

Assessment

Attendance