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Recursos bibliográficos en papel y digitales -
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» Resultado: 2 registros
| Registro 1 de 2 | |
| Autor: | Hsu, Li - Chen, Lu - Gorfine, Malka - Malone, Kathleen |
| Título: | Semiparametric estimation of marginal hazard function from case-control family studies |
| Fuente: | Biometrics. v.60, n.4. International Biometric Society |
| Páginas: | pp. 936-944 |
| Año: | dec. 2004 |
| Resumen: | Estimating marginal hazard function from the correlated failure time data arising from case-control family studies is complicated by noncohort study design and risk heterogeneity due to unmeasured, shared risk factors among the family members. Accounting for both factors in this article, we propose a two-stage estimation procedure. At the first stage, we estimate the dependence parameter in the distribution for the risk heterogeneity without obtaining the marginal distribution first or simultaneously. Assuming that the dependence parameter is known, at the second stage we estimate the marginal hazard function by iterating between estimation of the risk heterogeneity (frailty) for each family and maximization of the partial likelihood function with an offset to account for the risk heterogeneity. We also propose an iterative procedure to improve the efficiency of the dependence parameter estimate. The simulation study shows that both methods perform well under finite sample sizes. We illustrate the method with a case-control family study of early onset breast cancer. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
| Registro 2 de 2 | |
| Autor: | Yasui, Yutaka - Pepe, Margaret - Hsu, Li - Adam, Bao-Ling - Feng, Ziding - |
| Título: | Partially Supervised Learning Using an EM-Boosting Algorithm |
| Fuente: | Biometrics. v.60, n.1. International Biometric Society |
| Páginas: | pp. 199-206 |
| Año: | mar. 2004 |
| Resumen: | Training data in a supervised learning problem consist of the class label and its potential predictors for a set of observations. Constructing effective classifiers from training data is the goal of supervised learning. In biomedical sciences and other scientific applications, class labels may be subject to errors. We consider a setting where there are two classes but observations with labels corresponding to one of the classes may in fact be mislabeled. The application concerns the use of protein mass-spectrometry data to discriminate between serum samples from cancer and noncancer patients. The patients in the training set are classified on the basis of tissue biopsy. Although biopsy is 100 per cet specific in the sense that a tissue that shows itself to have malignant cells is certainly cancer, it is less than 100 per cent sensitive. Reference gold standards that are subject to this special type of misclassification due to imperfect diagnosis certainty arise in many fields. We consider the development of a supervised learning algorithm under these conditions and refer to it as partially supervised learning. Boosting is a supervised learning algorithm geared toward high-dimensional predictor data, such as those generated in protein mass-spectrometry. We propose a modification of the boosting algorithm for partially supervised learning. The proposal is to view the true class membership of the samples that are labeled with the error-prone class label as missing data, and apply an algorithm related to the EM algorithm for minimization of a loss function. To assess the usefulness of the proposed method, we artificially mislabeled a subset of samples and applied the original and EM-modified boosting (EM-Boost) algorithms for comparison. Notable improvements in misclassification rates are observed with EM-Boost. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
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