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R studio webinar4/14/2023 ![]() ![]() ![]() ![]() ![]() Note that, working on software during the webinar is not mandatory. Webinar does not provide any installation support. It is assumed that you have the following software packages installed. PopData BC link for the general description of the webinar.Research Article: The effectiveness of Right Heart Catheterization in the initial care of critically ill patients.To learn more about the research case example and related training dataset that will be used for this webinar session, please see the following web links: In the webinar, R will be the primary software package used to demonstrate the implementations. Attendees should have prerequisite knowledge of multiple regression analysis and working knowledge in R (e.g., basic data manipulation and regression fitting). (extra slides) Discuss the best practices associated with propensity score analysesīackground in causal inference is not required.Outline some extensions of this approach in solving complex real-world problems in the healthcare data analysis context (e.g., in longitudinal and big-data context).Explain assumptions and diagnostics of propensity score analyses.Demonstrate a propensity score analysis using R software and a sample dataset.Applying propensity score methods (e.g., matching and inverse probability weighting).Describe the basic concepts associated with causal inference and propensity score approaches.The session will outline how these analyses are different than conventional regression methods and will address key assumptions/diagnostics of these models. This webinar will focus on learning causal inference approaches in a healthcare data analysis context with a particular focus on explaining the application of propensity score analysis in a real-world data analysis context. Introduction to Causal Inference: Propensity Score Analysis in Healthcare Data Summary ![]()
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