Modern Methods in Data Analysis

This course provides statistical methods to study the association between (multiple) determinants and the occurrence of an outcome event. The course starts with an introduction to likelihood theory, using simple examples and a minimum of mathematics. Next, the most important regression models used in medical research are introduced. Topics are: maximum-likelihood methods, logistic regression, model validation and regression diagnostics, Poisson regression, and analysis of `event-history´ data, including an extensive discussion of the Cox proportional hazards regression model. Also, the basic principles of longitudinal data analysis are taught.

At the end of this course, the student:

• has insight in the principles of the likelihood theory and maximum likelihood methods
• understands the principles of the following statistical analysis techniques:
  • Logistic regression analysis
  • Poisson regression analysis
  • Analysis of event history data, including the Cox proportional hazard regression model
• has insight in model validation and regression diagnostics
• understands the principles of the following resampling techniques:
  • Bootstrapping
  • Permutation and randomization tests
• has insight in the basic principles of longitudinal data analysis
• is able to apply the above mentioned techniques using common statistical packages (SPSS, R)
• knows in which situations these techniques can be applied and the conditions that should be met to obtain reliable results using these techniques
• understands the results obtained with these techniques, and is able to apply these results in practice (e.g. in answering a study question)

Our courses are aimed at clinical researchers, nurses, general practitioners, and other health professionals who want to improve their skills in epidemiology, statistics and (clinical) research.

3 weeks, fulltime (face to face)