Methoden vergleichen

	Adaptive Survival Analysis	Kaplan-Meier-Schätzer
Fachgebiet	Epidemiologie	Statistik
Familie	Process / pipeline	Survival analysis
Entstehungsjahr	2000s (formalized ~2000–2006)	1958
Urheber	Bauer, Posch, and collaborators (adaptive design framework); Lachin & Foulkes (event-driven survival trial foundations)	Edward L. Kaplan and Paul Meier
Typ	Adaptive statistical design for time-to-event outcomes	Nonparametric estimator
Wegweisende Quelle	Bauer, P., & Posch, M. (2004). Modification of the sample size and the schedule of interim analyses in survival trials based on data inspections. Statistics in Medicine, 23(8), 1333–1353.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Aliasnamen	adaptive time-to-event analysis, adaptive event-driven trial analysis, adaptive hazard modeling, ASA	KM estimator, product-limit estimator, Kaplan-Meier curve, survival curve estimator
Verwandt	3	2
Zusammenfassung	Adaptive survival analysis integrates adaptive clinical trial design with time-to-event statistical methods, allowing pre-specified modifications to sample size, event targets, or allocation ratios at interim stages based on accumulating survival data. It is widely used in oncology, cardiovascular, and infectious disease research where the primary endpoint is a hazard-based outcome such as progression-free survival or all-cause mortality.	The Kaplan-Meier estimator is a nonparametric method for estimating the survival function S(t) — the probability that an individual survives beyond time t — from data that include censored observations. Introduced by Edward L. Kaplan and Paul Meier in their landmark 1958 JASA paper, it is the standard first step in any survival analysis and is among the most-cited statistical methods in biomedical research.