Learn to define dependent and independent variables, differentiate between univariate and multivariate analyses, and identify confounder factors through practical examples and sample datasets. Master the appraisal of causality between outcome variables and multiple exposure variables by examining collinearity, effect modifiers, and confounding factors. Develop skills in proposing appropriate modeling strategies for variable selection, identifying interactions and linear trends, and connecting results from multivariable analysis to table-based analytical techniques.
Overview
Syllabus
- Define the dependent and dependant variables
- Illustrate the confounder factors with an example
- Differentiate between univariate and multivariate analyses
- Demonstrate identification of confounder in a sample data set
- Define the dependent and dependant variables
- Appraise the causality between outcome variable and several exposure variables in terms of collinearity, effect modifiers, and confounding factors.
- Propose an appropriate modeling strategy to select variables, identify interaction and linear trends, and relate results from multivariable analysis to those from table-based techniques.
Taught by
Centre for Lifelong Learning
