So sánh phương pháp

	Phân tích sống còn tiến cứu	Phân tích sống còn
Lĩnh vực	Dịch tễ học	Thống kê nghiên cứu
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1958–1972 (foundational methods); prospective design emphasis formalized by 1980s	1958
Người khởi xướng	Kaplan & Meier (estimator, 1958); Cox (proportional hazards model, 1972); prospective design formalised in modern clinical epidemiology	Edward L. Kaplan and Paul Meier
Loại	Longitudinal observational or experimental study design with time-to-event analysis	Method
Công trình gốc	Kleinbaum, D. G., & Klein, M. (2012). Survival Analysis: A Self-Learning Text (3rd ed.). Springer. ISBN: 978-1441966452	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Tên gọi khác	prospective time-to-event analysis, prospective failure-time analysis, forward-looking survival study, prospective event-time study	Kaplan-Meier analysis, Cox regression, TTE analysis
Liên quan	5	3
Tóm tắt	Prospective survival analysis is a longitudinal study design in which participants are enrolled before the event of interest occurs, followed forward in time under standardised conditions, and analysed using survival-analytic methods to estimate the time until a defined clinical endpoint — such as death, disease recurrence, or treatment failure. Because data are collected prospectively, exposure and covariate information are recorded before outcomes are known, substantially reducing recall and selection bias relative to retrospective approaches.	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.