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Abstract:
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Observational studies are often conducted to discover real world evidence of comparative effectiveness using real world data, but selection bias in real world data poses threats to the validity of real world evidence. Propensity score methods (PSMs) have been increasingly used in observational studies as a means of reducing selection bias. This course will introduce concepts, applications, and issues of PSMs, including matching, stratification, and weighting. We will also discuss when and how to apply PSMs in observational studies using real world data. Through lectures on the concepts of PSMs and hands-on activities for the use of statistical programs in R and SAS, this course will benefit faculty members, graduate students, and applied researchers improving the quality of observational studies. Instructions for downloading and installing related statistical programs and examples of real world data will be provided to participants in advance through a course website, and additional handouts will be also made available in class. No prior knowledge of PSMs is required. However, an understanding of basic research design and statistics is preferred. Participants are encouraged to bring their own laptops for hands-on activities during which participants are also welcomed to work on their own real world data.
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