Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Evaluación Bayesiana de Pruebas de Cribado× | Estudio de Cohorte× | |
|---|---|---|
| Campo | Epidemiología | Epidemiología |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1763 (theorem); clinical screening application formalized ~1959–1970s | Mid-20th century (formal epidemiological design codified ~1950s) |
| Autor original≠ | Thomas Bayes (theorem, 1763); applied to clinical screening by Ledley & Lusted (1959) | Doll & Hill (British Doctors Study, 1951); Snow (cholera, 1854) |
| Tipo≠ | Bayesian analytical framework for test evaluation | Observational longitudinal study design |
| Fuente seminal≠ | Fletcher, R. H., Fletcher, S. W., & Fletcher, G. S. (2014). Clinical Epidemiology: The Essentials (5th ed.). Lippincott Williams & Wilkins. ISBN: 978-1451144475 | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| Alias | Bayesian diagnostic test evaluation, Bayesian predictive value analysis, posterior predictive value approach, Bayes theorem screening | longitudinal study, follow-up study, panel study, incidence study |
| Relacionados | 6 | 6 |
| Resumen≠ | Bayesian screening test evaluation applies Bayes' theorem to quantify how a screening test result changes the probability that an individual truly has a disease. Rather than reporting sensitivity and specificity in isolation, the approach centres on predictive values — the probability of disease given a positive or negative test — which depend critically on disease prevalence in the population being screened. The framework allows systematic updating of pre-test probability to post-test probability and supports decision-making under uncertainty. | A cohort study assembles a group of individuals who share a common starting point — typically freedom from the outcome of interest — and follows them over time to observe who develops the outcome. By comparing incidence rates between exposed and unexposed subgroups, researchers can estimate relative risk and absolute risk differences. Cohort studies are the gold-standard observational design for measuring disease incidence and establishing temporal relationships between exposure and outcome. |
| ScholarGateConjunto de datos ↗ |
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