Course Description

"Advanced Multivariate Statistical Methods in Healthcare Data Analysis" is designed to equip students with advanced skills in analyzing healthcare data using sophisticated statistical techniques. This comprehensive course delves into complex statistical methods that are widely employed in the healthcare industry, focusing on practical application while also providing a solid theoretical foundation.

The course is structured into four modules, each building upon the previous one, covering topics such as non-linear trends, interacting variables, outlier detection, and various forms of logistic regression. Through a blend of written content, video lectures, hands-on activities, and assessments, students will gain proficiency in applying these advanced statistical methods to real-world healthcare scenarios.

What Students Will Learn

• Implement and interpret nonlinear regressions using quadratic and logarithmic variables.
• Understand and utilize interactions between variables in regression models.
• Identify and manage problematic data points in regression analysis.
• Apply logistic regression models and interpret their outcomes.
• Conduct diagnostic tests to validate logistic regression models.
• Utilize and interpret ordinal, multinomial, and Poisson logistic regression models.
• Make data-informed decisions in real-world healthcare settings.

Prerequisites

To succeed in this course, students should have completed:

• DA-601: Introduction to Healthcare Data Analysis
• DA-602: Linear Relationship Data in Healthcare

Course Content

• Non-Linear Trends
• Interacting Variables and Finding Outliers
• Logistic Regression
• Logistic Regression Variants (Ordinal, Multinomial, and Poisson)
• Mathematical underpinnings and relevant formulae
• Assumptions necessary for understanding statistical methods
• Practical application of statistical techniques in healthcare settings

Who This Course Is For

• Healthcare professionals seeking to enhance their data analysis skills
• Data analysts working in the healthcare industry
• Students pursuing careers in healthcare data analytics
• Researchers interested in advanced statistical methods for healthcare studies
• Anyone looking to deepen their understanding of multivariate statistical techniques in a healthcare context

Real-World Applications

• Analyzing complex healthcare datasets to uncover non-linear relationships
• Identifying significant factors influencing health outcomes through interaction analysis
• Predicting patient outcomes using logistic regression models
• Evaluating the effectiveness of healthcare interventions
• Informing evidence-based decision-making in healthcare policy and practice
• Conducting robust research studies in healthcare and public health
• Improving healthcare quality and efficiency through data-driven insights

Syllabus