Target group: Postgraduate students and young researchers from the health sciences and related fields who want to work with regression models in their own research or wish to understand regression model based analyses found in the literature.
Topics covered:
All topics will be illustrated by examples from various fields of medical research like prognostic studies, epidemiological risk factor studies, experimental studies, diagnostic studies and observational studies.
Prerequisites: Only some basic knowledge of statistical terms like mean, p-value and confidence interval is required. The participants should have access to the Internet.
Registration fee: The registration fee depends on the source of the financial support for the participant:
| self paying | academic institution / non-profit organization | commercial sector | |
| including one year license of Stata | 450 EURO | 650 EURO | 1000 EURO |
| without Stata license | 400 EURO | 600 EURO | 900 EURO |
If organizations want to send 2 or more participants to the course, we offer reduced fees. Please contact us at info@isqr.uni-freiburg.de.
Registration: Starting November 15, 2011, you can register for the course at http://www.isqr.intercongress.de. You can pay directly with a credit card, or you can transfer your registration fee to a bank account.
Stipends: A few stipends are available for participants who cannot afford the registration fee. Please fill out the application form and send it to stipend@isqr.uni-freiburg.de. For further questions, please do not hesitate to contact us.
Practicalities: The course is offered as a web based distance learning course over a period of 16 weeks. The participants are expected to download and read about 25-35 pages of teaching material (pdf-files) each week. Examples from the teaching material can be found here. The teaching material covers an introduction into the concepts and methods as well as instructions how to apply the methods using the statistical package Stata. Each week there will be practical exercises, with solutions provided one week later. There will be a discussion list allowing communication among the participants and with the teaching staff. Any question to the teaching staff will be answered within 24 hours.
Use of Stata: A one year license of Stata 12 is included in the registration fee. Stata is available
for Windows, Unix and Mac. For further details visit the Stata home page.
Participants unfamiliar with Stata will get a
short introduction prior to the course.
The examples and exercises of the course require access to Stata 10, 11 or 12.
The Intercooled version of Stata is sufficient.
Course certificate: A course certificate is given to all participants, who participated in a multiple choice test and delivered a final written exercise.
Course credits: 8 ECTS
Teaching staff:
Werner Vach and Primrose Beryl, Clinical Epidemiology,
Institute of Medical Biometry and Medical Informatics, University of Freiburg
Course administration:
Per Berg and Monika Richards, International School of Quantitative
Research, University of Freiburg
email:
info@isqr.uni-freiburg.de
Important dates
| February 2, 2012 | Final date for registration of participants who need an introduction to Stata or a Stata license |
| February 16, 2012 | Start of one-week introduction to Stata |
| February 21, 2012 | Final date for registration of participants who have a Stata license |
| February 23, 2012 | Start of teaching period |
| June 28, 2012 | End of teaching period (Discussion list will be served until July 5) |
| June 28, 2012 | Multiple choice test and homework for course certificate will be available |
| August 23, 2012 | Final deadline for delivery of written exercise |
Course schedule: Each teaching week starts on a Thursday. The material for each week will be available already one the preceding Tuesday. The discussion of the contents of each teaching week on the discussion list should be limited to the teaching week and the following two weeks.
| Start date | Unit | Topics |
|---|---|---|
| February 23 | Week 1 | Why using regression models? |
| An introductory example | ||
| Adjusted effects | ||
| March 1 | Week 2 | Inference for the classical multiple regression model |
| Logistic regression | ||
| March 8 | Week 3 | Inference for the logistic regression model |
| Categorical covariates | ||
| March 15 | Week 4 | Handling ordered categories: A first lesson in regression modeling strategies |
| The Cox proportional hazard model | ||
| Common pitfalls in using regression models | ||
| March 22 | Week 5 | Some useful technicalities |
| Comparing regression coefficients | ||
| March 29 | Week 6 | Power and sample size I |
| April 5 | Easter break | |
| April 12 | Week 7 | Power and sample size II |
| The selection of the sample | ||
| April 19 | Week 8 | The selection of variables for a regression analysis |
| April 26 | Week 9 | Modelling non-linear effects I |
| May 3 | Week 10 | Modelling non-linear effects II |
| Transformation of covariates | ||
| May 10 | Week 11 | Effect modifications and interactions |
| May 17 | Week 12 | Applying regression models to clustered data |
| Applying regression models to longitudinal data | ||
| May 24 | Whitsun break | |
| May 31 | Week 13 | Risk scores |
| June 7 | Week 14 | Construction of predictors |
| Evaluating the predictive performance | ||
| June 14 | Week 15 | The impact of measurement error on a regression analysis |
| The impact of incomplete data on a regression analysis | ||
| Alternatives to regression modeling | ||
| June 21 | Week 16 | Specific regression models and specific usages of regression models |
| The goodness of a model | ||
| Regression modeling strategies |
Evaluation of previous courses: Some information about the evaluation of a previous version of this course can be found here.