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	Disegno Bayesiano Ex Post Facto	Studio di coorte retrospettivo
Campo	Disegno della ricerca	Epidemiologia
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1964 (Kerlinger ex post facto); Bayesian integration from 1990s–2000s onward	Mid-20th century (widely formalized 1950s–1970s)
Ideatore	Frederick N. Kerlinger (ex post facto framework); Bayesian extension draws on Laplace and modern Bayesian statistics	Systematic use attributed to early 20th-century occupational epidemiology; formalized in modern epidemiological theory by Brian MacMahon and others
Tipo	Quantitative observational research design with Bayesian inference	Observational analytic study
Fonte seminale	Kerlinger, F. N. (1973). Foundations of Behavioral Research (2nd ed.). Holt, Rinehart and Winston.	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641
Alias	Bayesian causal-comparative design, Bayesian after-the-fact design, Bayesian observational causal design, Bayesian retrospective causal study	historical cohort study, non-concurrent cohort study, retrospective follow-up study, historical prospective study
Correlati	5	6
Sintesi	Bayesian ex post facto design investigates possible causal relationships among variables that have already occurred, without researcher manipulation of those variables, and quantifies uncertainty about those relationships using Bayesian statistical inference. The researcher selects groups that differ on an outcome or a presumed cause after the fact, then uses prior knowledge and observed data together — via Bayes' theorem — to estimate credible effect sizes, group differences, or predictors.	A retrospective cohort study assembles a group of individuals who share a common starting point and reconstructs their exposure history and subsequent outcomes entirely from pre-existing records. Because the data have already been collected before the study begins, the design is far faster and cheaper than a prospective cohort; however, the researcher must work with whatever information was recorded at the time rather than collecting purpose-built measurements.