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Activity Number: 545 - Machine Learning and Nonparametric Methods in Causal Inference
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #312789
Title: Two-Stage Super Learner for Predicting Healthcare Expenditures
Author(s): Ziyue Wu* and David Benkeser and Seth Berkowitz
Companies: Emory University and Emory University and The University of North Carolina at Chapel Hill
Keywords: healthcare expenditure; two-part model; super learning; cross-validation

Healthcare utilization and associated costs have increased rapidly in recent years, making the study of healthcare expenditures an important area of public health research. Analysis of healthcare expenditure data is challenging due to heavily skewed distributions and zero-inflation. Myriad methods have been developed for analyzing cost data; however, a priori determination of an appropriate method is often difficult. Super-learning, a technique that considers an ensemble of methods for cost estimation, provides an interesting alternative for modeling healthcare expenditures. The super learner has demonstrated benefits over a single method in recent studies across many disciplines. In this work, we propose a two-stage super learner specifically designed for predicting zero-inflated expenditures. We demonstrate that the two-stage super learner has strong performance in predicting healthcare costs across a variety of cost distributions, in both real and simulated data.

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

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