Modern Methods in Data Analysis

Overview

The Modern Methods in Data Analysis course from Utrecht University will begin with an introduction to likelihood theory, using simple examples and a minimum of mathematics. You will then move to learning about the most important regression models used in medical research. These include logistic regression, Poisson regression, analysis of event history data, and the Cox proportional hazards regression model. In addition, you will become familiar with model validation and regression diagnostics, as well as with the basic principles of re-sampling methods and longitudinal data analysis.

The course is aimed at professionals who are interested in to learn more about statistics for medical research. However, a medical education is not a requirement to successfully participate in this course.

Learning objectives

By the end of the course, you should be able to:

• Explain the principles of the likelihood theory and maximum likelihood methods
• Explain the principles of the following statistical analysis techniques: Logistic regression analysis, Poisson regression analysis, Analysis of event history data, including the Cox proportional hazards regression model
• Explain model validation and regression diagnostics
• Describe the basic principles of longitudinal data analysis
• Apply the above-mentioned techniques using common statistical packages (e.g. SPSS or R)
• Name the situations in which these techniques can be applied and the conditions that should be met to obtain reliable results using these techniques
• Explain and interpret the results obtained with these techniques, and apply these results in practice (e.g. to answer a research question)