| Автор | Richardson, Sylvia |
| Автор | Gilks, Walter R. |
| Дата выпуска | 1993 |
| dc.description | Risk factors used in epidemiology are often measured with error which can seriously affect the assessment of the relation between risk factors and disease outcome. In this paper, a Bayesian perspective on measurement error problems in epidemiology is taken and it is shown how the information available in this setting can be structured in terms of conditional independence models. The modeling of common designs used in the presence of measurement error (validation group, repeated measures, ancillary data) is described The authors indicate how Bayesian estimation can be carried out in these settings using Gibbs sampling, a sampling technique which is being increasingly referred to in statistical and biomedical applications. The method is illustrated by analyzing a design with two measunng instruments and no validation group. Am J Epidemiol 1993; 138.430–42 |
| Формат | application.pdf |
| Издатель | Oxford University Press |
| Копирайт | © 1993 by The Johns Hopkins University School of Hygiene and Public Health |
| Тема | biometry |
| Тема | Bayesian method |
| Тема | epidemiologic methods |
| Тема | Monte Carlo method |
| Тема | ORIGINAL CONTRIBUTIONS |
| Название | A Bayesian Approach to Measurement Error Problems in Epidemiology Using Conditional Independence Models |
| Тип | research-article |
| Electronic ISSN | 1476-6256 |
| Print ISSN | 0002-9262 |
| Журнал | American Journal of Epidemiology |
| Том | 138 |
| Первая страница | 430 |
| Последняя страница | 442 |
| Аффилиация | Richardson Sylvia; Unité 170, Institut National de la Santé et de la Recherche Médicale |
| Аффилиация | Gilks Walter R.; Medical Research Council, Biostatistics Unit, Cambridge |
| Выпуск | 6 |
