Keynote Presentation |
Concurrent Sessions |
Poster Sessions
Short Courses (full day) | Short Courses (half day) | Tutorials | Practical Computing Demos | Closing General Session with Refreshments
Short Courses (full day) | Short Courses (half day) | Tutorials | Practical Computing Demos | Closing General Session with Refreshments
| Saturday, February 17 | ||
| T2 Applying Propensity Score Methods to Observational Studies Using R and SAS |
Sat, Feb 17, 2:00 PM - 4:00 PM
Eugene |
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| Instructor(s): Wei Pan, Duke University | ||
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Observational studies are common in applied settings but pose threats to the validity of causal inference due to selection bias in the data. Propensity score methods have been increasingly used as a means of reducing selection bias to enhance the causal claims. A training course on the application of propensity score methods to observational studies using commonly used statistical software would be beneficial for applied statisticians and researchers to improve the quality of their observational studies. With this objective, the proposed course will introduce basic concepts and practical issues of propensity score methods, including matching, stratification, and weighting; the instructors will facilitate hands-on activities of applying propensity score methods to observational studies with real-world examples using R and SAS. No prior knowledge of propensity score methods or computer programming is required. Participants are encouraged to bring their own laptop computers for hands-on activities.
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