Advanced Topics in Healthcare Data Analysis

In this course, you will learn about some of the advanced skills you will need for real-world healthcare data analysis. You will continue to practice these skills using the statistical programming software called R and examples from the healthcare industry. The topics covered in this course will help you to engage in the more advanced data wrangling that is often necessary for data analysis and to make data-informed decisions in the healthcare field. While the course focuses on application and the use of these statistical methods, there is some discussion of the mathematical underpinning, relevant formulae, and assumptions necessary for understanding the application of statistical methods. This self-paced course is comprised of written content, video content, step-by-step follow-along activities, and assessments to reinforce your learning (Assessments available to Verified Track learners only). The course is comprised of 6 modules that you should complete in order, as each subsequent module builds on the previous one. Module 1: Causal Inference and Tools for Model Specification Module 2: Matching to Reduce Model Dependence Module 3: Simpson's Paradox and Fixed Effects Module 4: Random Effects Module 5: Repeated Measures and Longitudinal Data Module 6: Missing Data and Bootstrapping

• Apply causal estimation using randomized controlled trials and difference-in-difference methods.
• Use matching to balance datasets for improved regression model results.
• Employ multi-level regressions with fixed and random effects and interpret their results.
• Implement various techniques for addressing missing data and small sample sizes in datasets used for regression models.
• Communicate the results of your analysis to others in simple language.

• DA-601: Introduction to Healthcare Data Analysis
• DA-602: Linear Relationship Data in Healthcare
• DA-603: Regression Models in Healthcare