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Recursos bibliográficos en papel y digitales -
- libros, artículos de revistas,
ponencias de eventos, etc. -
» Resultado: 3 registros
| Registro 1 de 3 | |
| 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 3 | |
| Autor: | Zheng, Yingye - Cai, Tianxi - Feng, Ziding - |
| Título: | Application of the time-dependent ROC curves for prognostic accuracy with multiple biomarkers |
| Fuente: | Biometrics. v.62, n.1. International Biometric Society |
| Páginas: | pp. 279-287 |
| Año: | mar. 2006 |
| Resumen: | The rapid advancement in molecule technology has led to the discovery of many markers that have potential applications in disease diagnosis and prognosis. In a prospective cohort study, information on a panel of biomarkers as well as the disease status for a patient are routinely collected over time. Such information is useful to predict patients’ prognosis and select patients for targeted therapy. In this article, we develop procedures for constructing a composite test with optimal discrimination power when there are multiple markers available to assist in prediction and characterize the accuracy of the resulting test by extending the time-dependent receiver operating characteristic (ROC) curve methodology (b11Heagerty, Lumley, and Pepe, 2000, Biometrics56, 337-344). We employ a modified logistic regression model to derive optimal linear composite scores such that their corresponding ROC curves are maximized at every false positive rate. We provide theoretical justification for using such a model for prognostic accuracy. The proposed method allows for time-varying marker effects and accommodates censored failure time outcome. When the effects of markers are approximately constant over time, we propose a more efficient estimating procedure under such models. We conduct numerical studies to evaluate the performance of the proposed procedures. Our results indicate the proposed methods are both flexible and efficient. We contrast these methods with an application concerning the prognostic accuracies of expression levels of six genes. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
| Registro 3 de 3 | |
| Autor: | Cai, Tianxi - Betensky, Rebecca A. - |
| Título: | Hazard regression for interval-censored data with penalized spline |
| Fuente: | Biometrics. v.59, n.3. International Biometric Society |
| Páginas: | pp. 570-579 |
| Año: | sep. 2003 |
| Resumen: | This article introduces a new approach for estimating the hazard function for possibly interval- and right-censored survival data. We weakly parameterize the log-hazard function with a piecewise-linear spline and provide a smoothed estimate of the hazard function by maximizing the penalized likelihood through a mixed model-based approach. We also provide a method to estimate the amount of smoothing from the data. We illustrate our approach with two well-known interval-censored data sets. Extensive numerical studies are conducted to evaluate the efficacy of the new procedure. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
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