Reducing Selection Bias Inverse Probability Weighting and Bin Bootstrapping
Reducing selection bias is a concern when analyzing observational datasets. IPW (inverse probability weighting) and PSBB (propensity score bin bootstrapping) are two methods used to address selection bias. This presentation will demonstrate how IPW and PSBB capture information from all patients with balance achieved for measured confounders via propensity score adjustment.
Dr. Zugui Zhang is the lead Biostatistician for the Value Institute of Christiana Care Health System and research assistant professor at Thomas Jefferson University. He has a broad background in health outcomes research, public health, epidemiology, and biostatistics. He has worked extensively on large international and national, multi-center, randomized clinical trials and observational studies.
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.
To see the full calendar of events, visit the Value Institute Events page or the ACCEL website, or subscribe to the ID Series mailing list.
Contact Sarahfaye Dolman at sarahfaye.f.dolman@christianacare.org with any questions.