Сравнение на методи

	Последователен / Групов последователен дизайн на изпитвания	Адаптивен дизайн на клинични изпитвания	Анализ на мощността
Област	Планиране на експеримента	Планиране на експеримента	Статистика
Семейство	Hypothesis test	Hypothesis test	Hypothesis test
Година на възникване	1979	1994	1969 (1st ed.); 1988 (seminal 2nd ed.)
Създател	O'Brien & Fleming; Pocock; Lan & DeMets	Bauer & Köhne	Jacob Cohen
Тип	Adaptive stopping trial design	Adaptive hypothesis test with interim analyses	Sample size and power planning
Основополагащ източник	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
Други названия	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
Свързани	3	3	5
Резюме	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.