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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: | Song, Xiao - Pepe, Margaret Sullivan - |
| Título: | Evaluating markers for selecting a patient’s treatment |
| Fuente: | Biometrics. v.60, n.4. International Biometric Society |
| Páginas: | pp. 874-883 |
| Año: | dec. 2004 |
| Resumen: | Selecting the best treatment for a patient’s disease may be facilitated by evaluating clinical characteristics or biomarker measurements at diagnosis. We consider how to evaluate the potential impact of such measurements on treatment selection algorithms. For example, magnetic resonance neurographic imaging is potentially useful for deciding whether a patient should be treated surgically for Carpal Tunnel Syndrome or should receive less-invasive conservative therapy. We propose a graphical display, the selection impact (SI) curve that shows the population response rate as a function of treatment selection criteria based on the marker. The curve can be useful for choosing a treatment policy that incorporates information on the patient’s marker value exceeding a threshold. The SI curve can be estimated using data from a comparative randomized trial conducted in the population as long as treatment assignment in the trial is independent of the predictive marker. Estimating the SI curve is therefore part of a post hoc analysis to determine whether the marker identifies patients that are more likely to benefit from one treatment over another. Nonparametric and parametric estimates of the SI curve are proposed in this article. Asymptotic distribution theory is used to evaluate the relative efficiencies of the estimators. Simulation studies show that inference is straightforward with realistic sample sizes. We illustrate the SI curve and statistical inference for it with data motivated by an ongoing trial of surgery versus conservative therapy for Carpal Tunnel Syndrome. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
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