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Activity Number: 447 - Recent Advances in Propensity Score Methods for Observational Studies with Multiple Treatments
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Health Policy Statistics Section
Abstract #300062
Title: Generalized Propensity Score Matching: Updates and Challenges Toward Establishing Best Practices
Author(s): Douglas Faries* and Zhanglin Cui and Li Li and Shu Yang and Shuhan Tang
Companies: Eli Lilly & Company and Eli Lilly & Company and Eli Lilly & Company and North Carolina State University and The Ohio State University
Keywords: observational; propensity

Propensity score methods are the gold standard for comparative analysis between two interventions from observational data, yet less well established for comparisons among three or more interventions. Conceptually the extensions of propensity scoring are sound, but practical issues make such difficult. Some researchers will resort to multiple regression or multiple pairwise comparisons. In pairwise analyses the common covariate support in each analysis may differ, complicating attempts to draw simultaneous inferences across all interventions. Also, when baseline covariate imbalances are large, regression methods rely on extrapolation and are highly sensitive to model assumptions. Yang et al (2016) developed a generalized propensity score (GPS) procedure that avoids the computational complexities involved when matching in many dimensions. However, practical issues remain, such as reliable variance estimation and identifying an optimal common covariate support. It is also unclear in which settings GPS matching may be superior to other methods. We will discuss recent research and remaining gaps to move toward best practices for observational analyses with three or more interventions.

Authors who are presenting talks have a * after their name.

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