Comparer des méthodes

	Conception adaptative d'essai	Analyse de survie
Domaine	Recherche clinique	Statistiques de recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s-2000s	1958
Auteur d'origine	Stephen Pocock, Christopher Jennison, and statistical methodologists; FDA formalized guidance 2019	Edward L. Kaplan and Paul Meier
Type	Research Design	Method
Source fondatrice	Pocock, S. J. (2005). Current issues in the design and interpretation of clinical trials. BMJ, 330(7500), 1118–1121.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Alias	adaptive trial, adaptive design, response-adaptive randomization, RAR	Kaplan-Meier analysis, Cox regression, TTE analysis
Apparentées	1	3
Résumé	An adaptive trial design allows pre-specified modifications to the trial based on interim data—such as sample size re-estimation, stopping for futility or efficacy, dropping ineffective arms, or shifting randomization ratios toward better-performing treatments. Developed systematically in the 1990s–2000s by statisticians like Pocock and Jennison, and formalized by the FDA in 2019, adaptive designs accelerate drug development, reduce exposure to ineffective treatments, and improve efficiency without inflating false-positive rates when properly executed.	Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters.