Advanced Biostatistics Multivariate Analysis

Overview

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This course develops multivariate biostatistical analysis, covering confounding, collinearity, effect modification, variable selection, interactions, and linear trends. Learners identify confounders in sample data and connect multivariable results with table-based 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.