Statistical modelling in biomedicine

Subject description

  • Formulation of models, estimation of parameters, interpretation
  • Interaction, relaxing the linearity assumption
  • Goodness-of-fit
  • Explained variation
  • Overfitting
  • Resampling, validation of models
  • Using R in statistical modelling


  • · Fitting the model: maximum likelihood, point and interval estimation of the odds ratio, test statistic, residuals, goodness of fit, influential points
  • · Interpretation of the model
  • · Evaluating predictive value of the model
  • ROC curves

The subject is taught in programs

Objectives and competences

The aim is for students to learn about strategies of statistical modelling, and to evaluate and validate a model, using logistic regression as an example.

Teaching and learning methods

Lectures and lab exercises, where students will learn to apply theoretical knowledge to real data. R software will be used.

Part of the pedagogical process will be carried out with the help of ICT technologies and the opportunities they offer.

Expected study results

Knowledge and understanding:

Students will be able to fit and interpret models, which will adequately fit the data.

Basic sources and literature

Steyerberg E. W. (2009): Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York: Springer.

Harrell F. E. (2001): Regression Modeling Strategies. New York: Springer.

Everitt B., Rabe-Hesketh S. (2001): Analyzing Medical Data Using S-PLUS. New York: Springer.

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