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| Bayesiläinen seulontatestien arviointi× | Bayesiläinen diagnostisen tarkkuuden tutkimus× | |
|---|---|---|
| Tieteenala | Epidemiologia | Epidemiologia |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1763 (theorem); clinical screening application formalized ~1959–1970s | 1995–2001 |
| Kehittäjä≠ | Thomas Bayes (theorem, 1763); applied to clinical screening by Ledley & Lusted (1959) | Joseph, Gyorkos & Coupal; Dendukuri & Joseph (formal Bayesian DTA framework) |
| Tyyppi≠ | Bayesian analytical framework for test evaluation | Bayesian inferential study design |
| Alkuperäislähde≠ | Fletcher, R. H., Fletcher, S. W., & Fletcher, G. S. (2014). Clinical Epidemiology: The Essentials (5th ed.). Lippincott Williams & Wilkins. ISBN: 978-1451144475 | Dendukuri, N., & Joseph, L. (2001). Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics, 57(1), 158–167. DOI ↗ |
| Rinnakkaisnimet | Bayesian diagnostic test evaluation, Bayesian predictive value analysis, posterior predictive value approach, Bayes theorem screening | Bayesian DTA study, Bayesian test evaluation, Bayesian diagnostic test accuracy, BDAS |
| Liittyvät | 6 | 6 |
| Tiivistelmä≠ | 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 Bayesian diagnostic accuracy study evaluates how well a medical test distinguishes between people who have a condition and those who do not, using Bayesian statistical methods that formally incorporate prior knowledge into the estimation of sensitivity, specificity, and related measures. Unlike classical approaches that rely solely on the observed sample, Bayesian inference combines a likelihood model of the data with prior probability distributions to produce posterior estimates with intuitive credible intervals. |
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