Compara mètodes

	Estudi Adaptatiu de Fase IV	Estudi Bayesiano de Fase IV
Camp	Epidemiologia	Epidemiologia
Família	Process / pipeline	Process / pipeline
Any d'origen	1990s–2000s (regulatory formalization of adaptive Phase IV designs)	1980s–1990s (formalized application to post-marketing settings)
Autor original	Adaptive design principles developed by multiple statisticians; Phase IV framework established by regulatory bodies (FDA, EMA) in the late 20th century	Donald A. Berry and colleagues (applied Bayesian framework to clinical trials)
Tipus	Adaptive post-marketing clinical study design	Observational or interventional post-marketing study with Bayesian inference
Font seminal	Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC. ISBN: 978-1584889625	Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756
Àlies	adaptive post-marketing surveillance study, adaptive pharmacovigilance study, adaptive Phase IV trial, adaptive post-approval study	Bayesian post-marketing surveillance study, Bayesian pharmacovigilance study, Bayesian post-approval study, Bayesian phase 4 trial
Relacionats	6	0
Resum	An Adaptive Phase IV study is a post-marketing surveillance study conducted after a drug or intervention has received regulatory approval, augmented with pre-specified adaptive design elements that allow pre-planned modifications to the study protocol in response to accumulating data. These modifications may include sample size re-estimation, endpoint adjustments, or population enrichment, all governed by statistical rules set before the study begins, preserving scientific integrity while increasing efficiency.	A Bayesian Phase IV study is a post-marketing research design that applies Bayesian statistical inference to accumulate evidence about a drug or device already approved for clinical use. By formally combining prior evidence from earlier development phases with emerging real-world data, it enables continuous, probabilistic updating of safety and effectiveness estimates — moving beyond the binary hypothesis tests of conventional frequentist surveillance.