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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: | Yasui, Yutaka - Feng, Ziding - Diehr, Paula - McLerran, Dale - Beresford, Shirley A. A. - McCulloch, Charles E. |
| Título: | Evaluation of community-intervention trials via generalized linear mixed models |
| Fuente: | Biometrics. v.60, n.4. International Biometric Society |
| Páginas: | pp. 1043-1052 |
| Año: | dec. 2004 |
| Resumen: | In community-intervention trials, communities, rather than individuals, are randomized to experimental arms. Generalized linear mixed models offer a flexible parametric framework for the evaluation of community-intervention trials, incorporating both systematic and random variations at the community and individual levels. We propose here a simple two-stage inference method for generalized linear mixed models, specifically tailored to the analysis of community-intervention trials. In the first stage, community-specific random effects are estimated from individual-level data, adjusting for the effects of individual-level covariates. This reduces the model approximately to a linear mixed model with the unit of analysis being community. Because the number of communities is typically small in community-intervention studies, we apply the small-sample inference method of Kenward and Roger (1997, Biometrics 53, 983-997) to the linear mixed model of second stage. We show by simulation that, under typical settings of community-intervention studies, the proposed approach improves the inference on the intervention-effect parameter uniformly over both the linearized mixed-effect approach and the adaptive Gaussian quadrature approach for generalized linear mixed models. This work is motivated by a series of large randomized trials that test community interventions for promoting cancer preventive lifestyles and behaviors. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
| Registro 2 de 3 | |
| 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 |
| Registro 3 de 3 | |
| Autor: | Qu, Yinsheng - Adam, Bao-ling - Thornquist, Mark - Potter, John D. - Thompson, Mary Lou - Yasui, Yutaka - Davis, John - Schellhammer, Paul F. - Cazares, Lisa - Clements, MaryAnn - Wright Jr., George L. - Feng, Ziding - |
| Título: | Data reduction using a discrete wavelet transform in discriminant analysis of very high dimensionality data |
| Fuente: | Biometrics. v.59, n.1. International Biometric Society |
| Páginas: | pp. 143-151 |
| Año: | mar. 2003 |
| Resumen: | We present a method of data reduction using a wavelet transform in discriminant analysis when the number of variables is much greater than the number of observations. The method is illustrated with a prostate cancer study, where the sample size is 248, and the number of variables is 48,538 (generated using the ProteinChip technology). Using a discrete wavelet transform, the 48,538 data points are represented by 1271 wavelet coefficients. Information criteria identified 11 of the 1271 wavelet coefficients with the highest discriminatory power. The linear classifier with the 11 wavelet coefficients detected prostate cancer in a separate test set with a sensitivity of 97 percent and specificity of 100 percent. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
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