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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: | Prentice, Ross L. - Pettinger, Mary - Anderson, Garnet L. - |
| Título: | Statistical issues arising in the Women’s Health Initiative |
| Fuente: | Biometrics. v.61, n.4. International Biometric Society |
| Páginas: | pp. 899-911 |
| Año: | dec. 2005 |
| Resumen: | A brief overview of the design of the Women’s Health Initiative (WHI) clinical trial and observational study is provided along with a summary of results from the postmenopausal hormone therapy clinical trial components. Since its inception in 1992, the WHI has encountered a number of statistical issues where further methodology developments are needed. These include measurement error modeling and analysis procedures for dietary and physical activity assessment; clinical trial monitoring methods when treatments may affect multiple clinical outcomes, either beneficially or adversely; study design and analysis procedures for high-dimensional genomic and proteomic data; and failure time data analysis procedures when treatment group hazard ratios are time dependent. This final topic seems important in resolving the discrepancy between WHI clinical trial and observational study results on postmenopausal hormone therapy and cardiovascular disease. |
| 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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