השוואת שיטות

	ניתוח רטרוספקטיבי של קפלן-מאייר – אמידת עקומת שרידות היסטורית	ניתוח הישרדות רטרוספקטיבי
תחום	אפידמיולוגיה	אפידמיולוגיה
משפחה	Process / pipeline	Process / pipeline
שנת המקור	1958 (method); retrospective application standard in clinical research since 1970s–1980s)	1970s–1980s (retrospective variant established)
הוגה השיטה	Edward L. Kaplan and Paul Meier	Kaplan & Meier (foundational estimator, 1958); Cox (regression model, 1972); retrospective application is a design variant documented since the 1970s
סוג	Non-parametric survival analysis applied to historical data	Retrospective observational analytical study
מקור מכונן	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.	Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press. ISBN: 978-1439856789
כינויים	retrospective KM analysis, retrospective survival curve estimation, historical Kaplan-Meier, retrospective KM estimator	historical survival study, retrospective time-to-event analysis, retrospective follow-up survival study, archival survival analysis
קשורות	5	5
תקציר	Retrospective Kaplan-Meier analysis applies the Kaplan-Meier product-limit estimator to time-to-event data drawn from existing records — medical charts, registries, or administrative databases — rather than from a prospectively followed cohort. The method estimates the probability of surviving (or remaining event-free) beyond any given time point while accounting for participants whose follow-up ended before the event occurred (censored observations). It is among the most commonly reported analyses in clinical oncology, cardiology, and surgery.	Retrospective survival analysis applies time-to-event statistical methods — most commonly the Kaplan-Meier estimator and Cox proportional hazards regression — to data collected from past records rather than through prospective follow-up. The researcher looks back at medical records, disease registries, or administrative databases to reconstruct each patient's journey from a defined starting point (e.g., diagnosis or surgery) to an outcome of interest (e.g., death, relapse, or hospital readmission), making it a cost-efficient approach for studying prognosis and risk factors when prospective follow-up is not feasible.