Módszerek összehasonlítása

	Pragmatic Survival Analysis	Túlélemzési módszerek
Tudományterület	Epidemiológia	Kutatási statisztika
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	Conceptual framework: 1967; widespread application: 1990s–2000s	1958
Megalkotó	Schwartz & Lellouch (explanatory vs. pragmatic distinction, 1967); extended in survival analysis literature from the 1970s onward	Edward L. Kaplan and Paul Meier
Típus	Observational / experimental hybrid — time-to-event analysis in real-world or pragmatic-trial settings	Method
Alapmű	Ford, I., & Norrie, J. (2016). Pragmatic Trials. New England Journal of Medicine, 375(5), 454–463.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Alternatív nevek	real-world survival analysis, pragmatic time-to-event analysis, effectiveness survival analysis, PSA	Kaplan-Meier analysis, Cox regression, TTE analysis
Kapcsolódó	5	3
Összefoglaló	Pragmatic survival analysis applies time-to-event statistical methods within pragmatic or real-world settings, estimating how long patients survive, remain event-free, or retain treatment benefit under conditions of routine clinical practice. Unlike explanatory survival analyses conducted under tightly controlled trial conditions, the pragmatic variant embraces the heterogeneity, treatment switching, non-adherence, and competing events that characterise real-world patient populations, prioritising external validity over internal precision.	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.