Administrative claims datasets present both challenges and benefits in the analysis of outcomes, cost, and comorbidity burden facing physically disabled individuals. The increasing use of administrative claims represents one of many plausible answers to concerns related to small sample and single institution studies. Longitudinal data analysis as well as outcomes and cost (insurance reimbursement and patient out-of-pocket cost as a form of financial burden) can be carefully studied using administrative claims datasets. In this webinar, we will highlight the strengths and analytic approaches in current work from the IDEAL RRTC. We will also carefully consider potential limitations in using these analytic approaches. Two different case studies will be considered with a cursory treatment of analytic approaches and population identification with inclusion and exclusion criteria with justification and rationale for these decisions. Applications of survival analysis, propensity score matching, and generalized linear models with respect to dichotomous and continuous and skewed outcomes will be discussed within the contexts of these case studies. The webinar will culminate with a summarization of the value of these studies, why they should continue to be pursued, and next steps in the evolution of claims-based research for the disabled population. The presenters, Dr. Elham Mahmoudi and Mr. Neil Kamdar, are both health services researchers with extensive experience working with administrative claims as well as other secondary data sources with a focus on applied econometric and statistical methods.

This webinar is part of the 2020 ICPSR Data Fair, "Data in Real Life." More information about the Data Fair can be found at http://myumi.ch/ICPSRdatafair2020. Please note that all attendees for this session must be registered for the ICPSR Data Fair.