• Startdatum:
  • 3 juni 2019
  • Cursusduur:
  • 5 dagen
  • Werkzaam als:
  • PhD, Student

Repeated measurements in clinical and epidemiological research

The course covers statistical methods to be used in the situation where one or more outcome variables are repeatedly measured in time on the same experimental unit. For instance, in a clinical trial, the outcome variable can be measured at baseline and at different times during the treatment period. For this type of data, traditional regression models cannot be used since outcomes of the same subject may be correlated, and this should be taken into account in the statistical model. Other examples of studies where there is a similar dependency in the outcome measurements are cluster randomised trials and meta-analysis. In the last one or two decades, much progress has been made in the development of new methods of analysis for dependent outcome data. In recent years several of these new methods have been implemented in commercially available computer packages. In the course, first an overview of classical approaches to repeated measurements will be given. Then modern methods are introduced. For approximately normally distributed response, focus will be on the General Linear Mixed Model. For non-normal response, the generalized estimating equations (GEE) approach for marginal models is discussed. Also some attention will be paid to random effects model, for instance random effects logistic regression. Examples of clinical and epidemiological applications will be given. In the computer labs, exercises with SPSS are used to get experience with applying these new methods to real data.Prerequisites

Familiarity with standard regression models such as the multiple linear regression and logistic regression model. No pre-knowledge of repeated measurements analysis is required.