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Regression Models as a Tool in Medical Research (2015)

A web based distance learning course


The participants should become familiar with the basic concepts and techniques in using regression models in medical research. They should be enabled to perform analyses of their own data, and to interpret, communicate and publish the results. They should also understand the basic potentials and limitations in using regression models and get some inspiration for a more effective and a more understandable use of regression models.


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:

  • Why using regression models? The basic interpretation and use of regression coefficients
  • The basic output from regression analyses: Effect estimates, confidence intervals and p-values
  • The basic regression models: Classical regression, logistic regression, Cox regression
  • Presenting the results of a regression analysis: Meaningful tables and graphs
  • Adjusted effects: Using regression models in the presence of confounding
  • Categorical and ordinal covariates: How to incorporate in a regression analysis
  • Common pitfalls in using regression models: Causality vs. association, relevance vs. significance, statistical models vs. real world models
  • Power: The key to distinguish useful from useless applications
  • Variable selection: Which variables should be in my model?
  • Effect modification and interactions: Assessment and interpretation
  • Non-linear effects: Some techniques and their meaningful use
  • Risk scores and predictions: Construction and assessment of precision
  • Incomplete data: Selecting a strategy to handle missing values
  • Measurement error: The impact on a regression analyses
  • Goodness of a model: How good is a model and how good need it to be?
  • Working with regression coefficients: How we can improve and support their interpretation
  • Modern techniques: Robust inference, clustered data, longitudinal data
  • Sample size and sample selection: How many subjects from which population?
  • Alternatives to regression models: Stratification, trees, correlation, and propensity score


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.



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 organisations want to send two or more participants to the course, we offer reduced fees. Please contact us at info@isqr.uni-freiburg.de.



Starting December 1, 2014, 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.



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.



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. The teaching material is closely related to the book "Regression Models as a Tool in Medical Research" by Werner Vach which was published by Chapman & Hall in 2013. 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 48 hours.


Use of Stata:

A one year license of Stata 13 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 11, 12 or 13. 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:



Teaching staff:

Werner Vach, 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: 

29.01.2015 Final date for registration of participants who need an introduction to Stata or a Stata license
12.02.2015 Start of one-week introduction to Stata
16.02.2015 Final date for registration of participants who have a Stata license
19.02.2015 Start of teaching period
02.07.2015 End of teaching period (Discussion list will be served until July 10)
02.07.2015 Multiple choice test and homework for course certificate will be available
10.09.2015 Final deadline for delivery of written homework exercise

Course schedule:

Each teaching week starts on a Thursday. The material for each week will be available already on 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. 

Date Week Topic
19.02.2015 1 Why using regression models?
    An introductory example
    Adjusted effects
26.02.2015 2 Inference for the classical multiple regression model
    Logistic regression
05.03.2015 3 Inference for the logistic regression model
    Categorical covariates
12.03.2015 4 Handling ordered categories: A first lesson in regression modeling strategies
    The Cox proportional hazard model
    Common pitfalls in using regression models
19.03.2015 5 Some useful technicalities
    Comparing regression coefficients
26.03.2015 6 Power and sample size I
02.04.2015   Spring break
09.04.2015 7 Power and sample size II
    The selection of the sample
16.04.2015 8 The selection of variables for a regression analysis
23.04.2015 9 Modelling non-linear effects I
30.04.2015   Easter break
07.05.2015 10 Modelling non-linear effects II
    Transformation of covariates
14.05.2015 11 Effect modifications and interactions
21.05.2015 12 Applying regression models to clustered data
    Applying regression models to longitudinal data
28.05.2015   Whitsun break
04.06.2015 13 Risk scores
11.06.2015 14 Construction of predictors
    Evaluating the predictive performance
18.06.2015 15 The impact of measurement error on a regression analysis
    The impact of incomplete data on a regression analysis
    Alternatives to regression modeling
25.06.2015  16 Specific regression models and specific usages of regression models
    The goodness of a model
    Regression modeling strategies


Evaluation of previous courses:

Evaluations of previous offers of this course can be found for 2011, 2012, 2013. and 2014.


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