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	Badania kohortowe bayesowskie	Badania podłużne
Dziedzina	Projektowanie badań	Projektowanie badań
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	Formalised in health research from the 1990s onward	Late 19th–early 20th century; methodologically codified through the 20th century
Twórca	Synthesis of cohort epidemiology (Doll & Hill, 1950s) with Bayesian inference (Bayes, Laplace, Jeffreys)	No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett
Typ	Quantitative longitudinal observational design	Quantitative (or mixed) observational research design
Źródło pierwotne	Ibrahim, J. G., & Chen, M. H. (2000). Power prior distributions for regression models. Statistical Science, 15(1), 46–60.	Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922841
Inne nazwy	Bayesian cohort study, Bayesian prospective cohort, Bayesian longitudinal cohort analysis, Bayesian follow-up study	longitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational study
Pokrewne	4	4
Podsumowanie	Bayesian cohort research follows a defined group of individuals over time to track outcomes, and uses Bayesian statistical inference to update beliefs about risk, incidence, or causal effects as follow-up data accumulate. Prior knowledge — from earlier studies, registries, or expert judgment — is formalised into a prior distribution and combined with the cohort's likelihood to yield a posterior distribution that quantifies uncertainty in a directly interpretable way.	Longitudinal research is an observational design in which the same participants, groups, or units are measured repeatedly over an extended period. Rather than capturing a single snapshot, it tracks change, stability, and temporal sequencing of variables — making it the primary non-experimental strategy for studying development, growth, decline, and the unfolding of causal processes across time.