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Abstract:
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Observational studies are often conducted for comparing treatment effects but they also pose threats to the validity of treatment effect estimation due to selection bias. Propensity score methods have been increasingly used in observational studies as a means of reducing selection bias so as to approximate the characteristics of randomized controlled trials. This course will introduce concepts and issues of propensity score methods, including matching, stratification, and weighting, and discuss when and how to use propensity score methods in observational studies. Through lectures on the theory and hands-on activities for the use of statistical software in R and Stata on real data, this course will benefit faculty members, graduate students, and applied researchers improving the quality of observational studies for comparing treatment effects. Instructions for downloading and installing related statistical software and example data will be provided to participants in advance through a course website, and handouts will be also available in class. No prior knowledge of propensity score methods is required. However, an understanding of basic research design and statistics is desirable. Participants are encouraged to bring their own laptops for hands-on activities during which participants are also welcome to work on their own data.
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