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Activity Number:
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251
- SPEED: Biopharmaceutical Methods and Application I, Part 2
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Type:
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Contributed
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Date/Time:
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Monday, July 29, 2019 : 2:00 PM to 2:45 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract #307612
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Title:
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The Application of Beta Regression for Modeling a Covariate Adjusted ROC
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Author(s):
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Xing Meng* and Jack D. Tubbs
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Companies:
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Baylor University and Baylor University
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Keywords:
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Placement values;
beta regression;
ROC regression
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Abstract:
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The receiver operating characteristic (ROC) curve illustrates the diagnostic capabilities of the binary classifier system. In many applications, the test performance is affected by covariates. A solution to this problem can be found by modeling the ROC as a function of the covariate effects using a generalized linear model framework. There are three common models that can be used to estimate a covariate-adjusted ROC. These include a parametric, semiparametric and beta regression model. The beta regression model has been shown to perform very well using the placement values, the probability that the test result of the diseased subject exceeds the test result of the non-diseased subject with the same covariate value. The beta regression model also avoids the correlation problem found in the parametric and semiparametric methods. This paper will use data from a clinic study to compare the performance of the three models.
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Authors who are presenting talks have a * after their name.
