Robust Estimation of Propensity Score in Observational Study
Observational Study aims to draw inference about the possible effect of a treatment on subjects from an empirical comparison of treated and controlled groups and is widely used in healthcare and medical research when clinical trials are not applicable in practice. The central issue in observational study is to identify potential confounding to ensure the competing groups are comparable after suitable adjustment based on collected data. In this presentation, I will focus on the robustness in estimation of propensity score, which is the most popular method in practice to remove selection biases in observational study in order to enhance validity and generalization for both parametric and non-parametric models.
Dr. Pan Wu is the Senior Biostatistician for the Value Institute at the Christiana Care Health System and Research Assistant Professor in the Department of Medicine, Sidney Kimmel Medical School, Thomas Jefferson University. He has participated in research from a wide range of fields including psychosocial interventions, cardiovascular diseases, woman’s health, and health policy. His research interests include Causal Inference, Medication Analysis, Longitudinal Study with Missing Data, latent mixture models, data mining, and high-dimensional variable selection.
The Innovative Discoveries Series, sponsored by the Delaware Clinical & Translational Science ACCEL program and the Christiana Care Value Institute, features informal presentations on topics relevant to current research and healthcare practice, led by knowledgeable and experienced presenters. There are offerings for researchers, healthcare providers, and community members of varying levels of experience.
These free talks are held Fridays at noon at Christiana Hospital but can be viewed from your home or office computer. Earn CMEs by participating in-person or online. Lunch is served and all are welcome to attend.
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Contact Sarahfaye Dolman at sarahfaye.f.dolman@christianacare.org with any questions.