Advanced Biostatistics Multivariate Analysis

Overview

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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.

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.