Σύγκριση μεθόδων

	Σχεδιασμός διαδοχικών / ομαδικών διαδοχικών δοκιμών	Προσαρμοστικός Σχεδιασμός Κλινικών Δοκιμών	Ανάλυση Ισχύος
Πεδίο	Πειραματικός Σχεδιασμός	Πειραματικός Σχεδιασμός	Στατιστική
Οικογένεια	Hypothesis test	Hypothesis test	Hypothesis test
Έτος προέλευσης	1979	1994	1969 (1st ed.); 1988 (seminal 2nd ed.)
Δημιουργός	O'Brien & Fleming; Pocock; Lan & DeMets	Bauer & Köhne	Jacob Cohen
Τύπος	Adaptive stopping trial design	Adaptive hypothesis test with interim analyses	Sample size and power planning
Θεμελιώδης πηγή	O'Brien, P.C. & Fleming, T.R. (1979). A Multiple Testing Procedure for Clinical Trials. Biometrics, 35(3), 549–556.	Bauer, P. & Köhne, K. (1994). Evaluation of Experiments with Adaptive Interim Analyses. Biometrics, 50(4), 1029–1041.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
Εναλλακτικές ονομασίες	group sequential design, adaptive stopping design, Ardışık Deneme Tasarımı (Sequential / Group Sequential)	adaptive design, group sequential design, sample size re-estimation, platform trial	sample size calculation, power calculation, sensitivity analysis, a priori power analysis
Συναφείς	3	3	5
Σύνοψη	Sequential and group sequential trial designs allow a study to be stopped early — or continued — based on interim analyses conducted as data accumulate. The core framework was formalised by O'Brien and Fleming in 1979 and extended by Lan and DeMets's alpha-spending approach, and it controls the overall Type I error rate across all planned looks by pre-specifying both efficacy and futility boundaries before enrolment begins.	Adaptive clinical trial design is a flexible experimental framework, formalised by Bauer and Köhne in 1994, in which pre-specified rules allow the trial to be modified mid-course — adjusting sample size, treatment arms, or randomisation ratios — based on accumulating interim data while rigorously controlling the Type I error rate.	Power analysis is a planning and evaluation technique that quantifies the probability of detecting a real effect of a given magnitude at a chosen significance level. It links four quantities — sample size, effect size, significance level (alpha), and statistical power (1 minus beta) — so that researchers can determine the sample size needed before data collection or evaluate the sensitivity of a completed study.