While most medical studies aim to explain some phenomenon, a significant proportion does not have this as a primary goal. These studies instead aim to predict a certain event or measure as accurately as possible given a number of predictors. Although explaining and predicting are two separate goals, they are often interchanged in medical studies. More importantly, the statistical approaches used for these two types of data analyses are not the same.


      1. The basic differences between explanatory and predictive studies.
      2. Model estimation: variable selection, variable predictive power and penalisation (R-squared, AIC and BIC).
      3. Model Performance: N-fold cross validation, geographical and temporal validation, external validation.
      4. Model validity: Calibration and calibration slope, receiver operating characteristic (ROC) curve and area under the curve (AUC), prediction intervals

Upon completion of the course, the student will be able to distinguish between predictive and explanatory data analyses, as well as understand the basic statistical tools used in predictive studies.


Moons K, Royston P, Vergouwe Y, Grobbee D, Altman D. Prognosis and prognostic research: what, why and how? BMJ2008;b375.

Ewout W. Steyerberg, Yvonne Vergouwe; Towards better clinical prediction models: seven steps for development and an ABCD for validation, European Heart Journal, Volume 35, Issue 29, 1 August 2014, Pages 1925–1931,


Evaluation of the course will be based on active participation during the course.

Organizer: Lasse Hjort Jakobsen, Jan Brink Valentin

Lecturers: Lasse Hjort Jakobsen, Jan Brink Valentin


Time: 19-21 March 2019

Place: Room 405, Forskningens Hus, Sdr. Skovvej 15

Zip code:


Number of seats:

Deadline: 26 February