| Autor: |
Sarah Stanley, Jack D. Tubbs |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
Science Journal of Applied Mathematics and Statistics. 6:110 |
| ISSN: |
2376-9491 |
| DOI: |
10.11648/j.sjams.20180604.11 |
| Popis: |
Background: Several regression methodologies have been developed to model the ROC as a function of covariate effects within the generalized linear model (GLM) framework. In this article, we present an alternative to two existing parametric and semi-parametric methods for estimating a covariate adjusted ROC. The existing methods utilize GLMs for binary data when the expected value equals the probability that the test result for a diseased subject exceeds that of a non-diseased subject with the same covariate values. This probability is referred to as the placement value. Objective: The new method directly models the placement values through beta regression. Methods: We compare the proposed method to the existing models with simulation and a clinical study. Conclusion: The proposed method performs favorably with the commonly used parametric method and has better performance than the semi-parametric method when modeling the covariate adjusted ROC regression. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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