Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовская иерархическая модель× | Дизайн «случай-кроссовер»× | |
|---|---|---|
| Область≠ | Байесовские методы | Эпидемиология |
| Семейство≠ | Bayesian methods | Process / pipeline |
| Год появления≠ | 2006 | 1991 |
| Автор метода≠ | Gelman & Hill (2006); Bayesian multilevel tradition | Malcolm Maclure |
| Тип≠ | hierarchical probabilistic model | Observational epidemiological study design |
| Основополагающий источник≠ | Gelman, A. & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. DOI ↗ | Maclure, M. (1991). The case-crossover design: A method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153. DOI ↗ |
| Другие названия≠ | multilevel Bayes, Bayesian multilevel model, Bayesian HLM, partial pooling model | case-crossover study, CCO design, self-matched case study, within-person crossover case study |
| Связанные≠ | 4 | 3 |
| Сводка≠ | Bayesian hierarchical modelling, popularised by Gelman and Hill (2006), is a Bayesian approach to nested data structures — such as students within schools within districts — that estimates separate parameters at each level while allowing those levels to share statistical strength through a mechanism called partial pooling. Where a classical hierarchical linear model treats group means as fixed unknown quantities, the Bayesian version places hyperprior distributions on those group means so that information flows freely across levels, producing more reliable group-level estimates whenever any individual group has few observations. | The case-crossover design is an observational epidemiological method that estimates whether a transient exposure triggers an acute event by comparing each case's exposure during a brief hazard window immediately before the event to their own exposure during earlier control periods. Because each person serves as their own control, all stable personal characteristics are automatically adjusted for, making the design especially powerful for studying intermittent exposures and sudden-onset outcomes such as myocardial infarction, stroke, or injury. |
| ScholarGateНабор данных ↗ |
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