Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Análise de Sobrevivência Retrospectiva× | Análise de Kaplan-Meier× | |
|---|---|---|
| Área | Epidemiologia | Epidemiologia |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1970s–1980s (retrospective variant established) | 1958 |
| Autor original≠ | Kaplan & Meier (foundational estimator, 1958); Cox (regression model, 1972); retrospective application is a design variant documented since the 1970s | Edward L. Kaplan and Paul Meier |
| Tipo≠ | Retrospective observational analytical study | Nonparametric survival estimator |
| Fonte seminal≠ | Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press. ISBN: 978-1439856789 | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Outros nomes | historical survival study, retrospective time-to-event analysis, retrospective follow-up survival study, archival survival analysis | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| Relacionados | 5 | 5 |
| Resumo≠ | 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. | Kaplan-Meier (KM) analysis is a nonparametric method for estimating the survival function from time-to-event data. Introduced by Kaplan and Meier in 1958, it produces the classic step-function survival curve that shows the probability of surviving beyond each observed event time, correctly accounting for censored observations — participants who left the study or had not yet experienced the event by the end of follow-up. It is one of the most widely used techniques in clinical and epidemiological research. |
| ScholarGateConjunto de dados ↗ |
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