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Recursos bibliográficos en papel y digitales -
- libros, artículos de revistas,
ponencias de eventos, etc. -
» Resultado: 2 registros
| Registro 1 de 2 | |
| Autor: | Pepe, Margaret Sullivan - Cai, Tianxi - Longton, Gary - |
| Título: | Combining predictors for classification using the area under the receiver operating characteristic curve |
| Fuente: | Biometrics. v.62, n.1. International Biometric Society |
| Páginas: | pp. 221-229 |
| Año: | mar. 2006 |
| Resumen: | No single biomarker for cancer is considered adequately sensitive and specific for cancer screening. It is expected that the results of multiple markers will need to be combined in order to yield adequately accurate classification. Typically, the objective function that is optimized for combining markers is the likelihood function. In this article, we consider an alternative objective function - the area under the empirical receiver operating characteristic curve (AUC). We note that it yields consistent estimates of parameters in a generalized linear model for the risk score but does not require specifying the link function. Like logistic regression, it yields consistent estimation with case-control or cohort data. Simulation studies suggest that AUC-based classification scores have performance comparable with logistic likelihood-based scores when the logistic regression model holds. Analysis of data from a proteomics biomarker study shows that performance can be far superior to logistic regression derived scores when the logistic regression model does not hold. Model fitting by maximizing the AUC rather than the likelihood should be considered when the goal is to derive a marker combination score for classification or prediction. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
| Registro 2 de 2 | |
| Autor: | Sullivan Pepe, Margaret - Longton, Gary - Anderson, Garnet L. - Schummer, Michel |
| Título: | Selecting differentially expressed genes from microarray experiments |
| Fuente: | Biometrics. v.59, n.1. International Biometric Society |
| Páginas: | pp. 133-142 |
| Año: | mar. 2003 |
| Resumen: | High throughput technologies, such as gene expression arrays and protein mass spectrometry, allow one to simultaneously evaluate thousands of potential biomarkers that could distinguish different tissue types. Of particular interest here is distinguishing between cancerous and normal organ tissues. We consider statistical methods to rank genes (or proteins) in regards to differential expression between tissues. Various statistical measures are considered, and we argue that two measures related to the Receiver Operating Characteristic Curve are particularly suitable for this purpose. We also propose that sampling variability in the gene rankings be quantified, and suggest using the "selection probability function," the probability distribution of rankings for each gene. This is estimated via the bootstrap. A real dataset, derived from gene expression arrays of 23 normal and 30 ovarian cancer tissues, is analyzed. Simulation studies are also used to assess the relative performance of different statistical gene ranking measures and our quantification of sampling variability. Our approach leads naturally to a procedure for sample-size calculations, appropriate for exploratory studies that seek to identify differentially expressed genes. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
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