Erasmus Summer Programme Courses

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Logistic Regression [ESP66]

Course highlights

EC points

1.4

Start date

3 August 2020

End date

7 August 2020

Course days

Monday to Friday (5 full days)

Course time

From 8:45 till 16:00 CEST

Faculty

Prof. Stanley Lemeshow

Course fee

€ 1660

Location

Online

Level

Intermediate

Prerequisites

A solid first course on applied statistics, including regression analysis.

Disciplines

  • Biostatistics
  • Advanced Statistics

Course Materials

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

Recommended book: Hosmer D, Lemeshow S, Sturdivant, RX (2013). Applied Logistic Regression, 3rd Ed. A Wiley-Interscience Publication, John Wiley & Sons Inc., New York, NY

A laptop is recommended. Access to STATA will be required and a software licence will be provided before the start of the course.

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Detailed information about this course:

Description

Faculty: Prof. Stanley Lemeshow, PhD


This course provides theoretical and practical training for biostatisticians, epidemiologists and professionals of related disciplines in statistical modeling with particular emphasis on logistic regression. The increasingly popular logistic regression model has become the standard method for regression analysis of binary, multinomial and ordinal response data in the health sciences.

Timeline:

10.00 - 12.00 hrs Practicum (option to choose morning or evening Practicum)

13.00 - 17.00 hrs Classes

17.00 - 19.00 hrs Practicum (option to choose morning or evening Practicum)


//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

Participants will learn to understand the logistic regression and be able to apply it to real data sets. Upon completion of the course, students will be able to:

  1. Understand the theoretical underpinnings of the logistic regression model
  2. Understand the interpretation of the coefficients in the model
  3. Understand the similarities and differences between logistic regression and classical contingency table analysis
  4. Learn how to use the model to control for confounding and interpret effect modification
  5. Learn how to assess model performance
  6. Learn to model response variables that have more than two outcome levels

Participant profile

Epidemiologists, biostatisticians, researchers in clinical medicine, researchers in the social sciences.

Assessment

Attendance