Related Experiment Video
Updated: Mar 11, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Multilevel regression modelling to investigate variation in disease prevalence across locations
Gudrun Weinmayr1, Jens Dreyhaupt1, Andrea Jaensch1
1Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany.
Abstract:
In this article, we show how to investigate the role of individual (personal) risk factors in outcome prevalence in multicentre studies with multilevel modelling. The variation in outcome prevalence is modelled by introducing a random intercept. In the next step, the empty model is compared with the model containing the risk factor(s). Because the outcome is dichotomous, this comparison can only be carried out after having rescaled the models' parameter values to the variance of an underlying continuous variable. We illustrate this approach with data from Phase Two of the International Study of Asthma and Allergies in Childhood (ISAAC) and provide a corresponding Stata do-file.
Related Concept Videos
Steps in Outbreak Investigation
Mechanistic Models: Compartment Models in Individual and Population Analysis
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Confounding in Epidemiological Studies
Study Designs in Epidemiology
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
Bias in Epidemiological Studies
