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	Sperimentazione clinica adattativa di Fase III	Sperimentazione Clinica Bayesiana di Fase III
Campo	Epidemiologia	Epidemiologia
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1969–2019 (sequential testing roots ~1969; formal adaptive design guidance 2010–2019)	1990s–2000s (widespread application)
Ideatore	Methodological foundations by Armitage et al. (1969); modern adaptive framework codified by FDA and ICH guidance (2010s)	Donald A. Berry; David J. Spiegelhalter (formalization in clinical context)
Tipo	Interventional confirmatory clinical trial with pre-specified interim adaptations	Confirmatory randomized controlled trial with Bayesian inference
Fonte seminale	International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). (2019). ICH E9(R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials. ICH Harmonised Guideline.	Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756
Alias	adaptive confirmatory trial, seamless Phase II/III adaptive trial, adaptive pivotal trial, adaptive design Phase III	Bayesian confirmatory trial, Bayesian RCT Phase III, Bayesian pivotal trial, BayesCT
Correlati	6	5
Sintesi	An adaptive Phase III clinical trial is a confirmatory randomized controlled trial that incorporates pre-specified rules allowing modifications to the trial design — such as sample size re-estimation, dose selection, or population enrichment — based on accumulating interim data, while preserving the Type I error rate. It sits at the top of the evidence hierarchy and is used to obtain regulatory approval of new interventions.	A Bayesian Phase III clinical trial is a large-scale, confirmatory randomized controlled trial that uses Bayesian statistical inference rather than conventional frequentist hypothesis testing to evaluate whether an experimental treatment meets pre-defined efficacy and safety thresholds. By combining prior evidence with accumulating trial data, it quantifies the probability that the treatment effect exceeds a clinically meaningful threshold, enabling more transparent decision-making under uncertainty.