Advanced Methods Webinar - Introduction to Causal Inference: Propensity Score Analysis in Healthcare Data
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. The session will outline how these analyses are different than conventional regression methods and will address key assumptions/diagnostics of these models.
The webinar will:
- Describe the basic concepts associated with causal inference and propensity score approaches
- Review guidelines for applying propensity score methods (e.g., matching and inverse probability weighting)
- Demonstrate a propensity score analysis using R software and a sample training dataset
- Explain assumptions and diagnostics of propensity score analyses
- Discuss the best practices associated with propensity score analyses
- 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).
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:
The effectiveness of Right Heart Catheterization in the initial care of critically ill patients.
Details about the training dataset:
Link to download the training dataset:
The recorded video presentation posted below is a condensed version of the live webinar presented on May 14th, 2020. Voiceover has been used in parts of the video to enhance sound quality.
Slides from the presentation are viewable online at: , The webinar website for Dr. Ehsan Karim's presentation is: https://ehsank.com/workshops/wbmaterials/ (This page includes a data link and webinar description).
There is also a dedicated GitHub page: https://ehsanx.github.io/popdataBCwebinar/ (This page consists of all of the R code/RMD files used in the webinar).
Dr. M. Ehsan Karim is an Assistant Professor at the UBC School of Population and Public Health, and a Scientist at the Centre for Health Evaluation and Outcome Sciences (CHÉOS), St. Paul's Hospital. He obtained his Ph.D. in Statistics from UBC, completed his postgraduate training in the Department of Epidemiology, Biostatistics, and Occupational Health at McGill, and was also a trainee at the Canadian Network for Observational Drug Effect Studies (CNODES). His current program of research focuses on developing causal inference methodologies and applications of data science approaches in the large healthcare data context in answering real-world questions, supported by the Michael Smith Foundation for Health Research Scholar award, grants from NSERC and BC SUPPORT Unit.