The training was designed to explain the principles of multivariable and multilevel analysis and provide fellows with the basic definitions and concepts for different types of univariable and multivariable regression models, including linear, logistic and conditional logistic, Poisson, negative binomial, and Cox regression.
They learned how to formulate a specific research question, decide on the appropriate analytical approach, and identify confounding.
The fellows engaged in practical analytical data exercises using R software, which included selecting the correct multivariable method and identifying relevant variables in order to build an optimal regression model.
The module drew on the expertise and experience of nominated external experts from across Europe and the MediPIET partner countries. The training methods included individual study prior to the module, lectures and discussions, case study-based group work, and Q&A sessions.
After completing this training, participants should be able to use multivariable statistical methods to investigate outbreaks and to perform applied research, including assessing public health findings to inform policy decisions and communication.
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