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| Analisi retrospettiva di Kaplan-Meier× | Analisi di sopravvivenza retrospettiva× | |
|---|---|---|
| Campo | Epidemiologia | Epidemiologia |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1958 (method); retrospective application standard in clinical research since 1970s–1980s) | 1970s–1980s (retrospective variant established) |
| Ideatore≠ | 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 |
| Tipo≠ | Non-parametric survival analysis applied to historical data | Retrospective observational analytical study |
| Fonte seminale≠ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ | Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press. ISBN: 978-1439856789 |
| Alias | 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 |
| Correlati | 5 | 5 |
| Sintesi≠ | 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. |
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