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Activity Number:
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154
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #309750 |
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Title:
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An Additive Model for a Heavily Right-Skewed Outcome
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Author(s):
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Amanda Allshouse*+ and Jianmin Wang and Jasmine Mathias and William Irish
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Companies:
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University of Colorado at Denver and Health Sciences Center and RTI Health Solutions and RTI Health Solutions and RTI Health Solutions
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Address:
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4200 East Ninth Avenue, C245HSC, Denver, CO, 80262,
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Keywords:
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Cost model ; healthcare cost modeling ; Skewed outcomes ; health econometrics
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Abstract:
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When estimating and modeling healthcare costs, challenges arise rooted in the heavily right-skewed nature of the distribution of non-zero annual costs. No consensus currently exists regarding the most appropriate method for analyzing heavily right-skewed data bounded at zero. Traditional approaches to this problem include fitting the logarithmic-transformed data with an ordinary least squares regression, resulting in a multiplicative model when an additive interpretation is desired. An additive model to accommodate skewness and heterogeneity has been developed. Heavily right-skewed non-zero data were generated to emulate health-care costs, and the statistical properties of the additive model compared to the traditional approach were evaluated using simulation techniques.
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