Friday, March 6, 2020 — 10:30 AM EST

Analysis of Prescription Drug Utilization with Beta Regression Models

The healthcare sector in the U.S. is complex and is also a large sector that generates about 20% of the country's gross domestic product. Healthcare analytics has been used by researchers and practitioners to better understand the industry. In this paper, we examine and demonstrate the use of Beta regression models to study the utilization of brand name drugs in the U.S. in order to understand variability of brand name drug utilization across different areas. The models are fitted to public datasets obtained from the Medicare & Medicaid Services and  the Internal Revenue Service. Integrated Nested Laplace Approximation (INLA) is used to perform the inference. The numerical results show that Beta regression models are able to fit the brand name drug claim rates well and including spatial dependence improves the performance of the Beta regression models. 

Friday, March 20, 2020 — 10:30 AM EDT

The Efficiency of Voluntary Risk Classification in Insurance Markets

It has been established that categorical discrimination based on observable characteristics such as gender, age, or ethnicity enhances efficiency. We consider a different form of risk classification when there exists a costless yet imperfectly informative test of risk type, with the test outcome unknown to the agents ex-ante. We show that a voluntary risk classification in which agents are given the option to take the test always increases efficiency compared with no risk classification. Moreover, voluntary risk classification also Pareto dominates a regime of compulsory risk classification in which all agents are required to take the test.

Friday, April 3, 2020 — 10:30 AM EDT

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Friday, May 1, 2020 — 3:00 PM to Sunday, May 3, 2020 — 6:00 PM EDT
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Friday, May 15, 2020 — 10:30 AM EDT

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