Salīdzināt metodes

	Adaptīvā izdzīvošanas analīze	Koksa proporcionālo risku modelis
Nozare	Epidemioloģija	Epidemioloģija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2000s (formalized ~2000–2006)	1972
Autors	Bauer, Posch, and collaborators (adaptive design framework); Lachin & Foulkes (event-driven survival trial foundations)	Sir David Roxbee Cox
Tips	Adaptive statistical design for time-to-event outcomes	Semi-parametric regression model
Pirmavots	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.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
Citi nosaukumi	adaptive time-to-event analysis, adaptive event-driven trial analysis, adaptive hazard modeling, ASA	Cox regression, Cox PH model, proportional hazards model, CPH
Saistītās	3	5
Kopsavilkums	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 Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research.