Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Análise de Sobrevivência Meta-analítica× | Análise de Kaplan-Meier× | |
|---|---|---|
| Área | Epidemiologia | Epidemiologia |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1990s–2000s (formalized ~1998) | 1958 |
| Autor original≠ | Parmar, Torri & Stewart (statistical framework); broader IPD tradition developed by the Early Breast Cancer Trialists' Collaborative Group | Edward L. Kaplan and Paul Meier |
| Tipo≠ | Quantitative synthesis / meta-analytic method | Nonparametric survival estimator |
| Fonte seminal≠ | Parmar, M. K. B., Torri, V., & Stewart, L. (1998). Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Statistics in Medicine, 17(24), 2815–2834. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Outros nomes | meta-analysis of time-to-event data, pooled survival analysis, IPD survival meta-analysis, aggregate survival meta-analysis | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| Relacionados≠ | 4 | 5 |
| Resumo≠ | Meta-analytic survival analysis is a quantitative synthesis method that pools hazard ratios and related time-to-event statistics from multiple independent studies to produce a single, more precise estimate of a treatment or exposure effect on survival outcomes such as overall survival, disease-free survival, or time to relapse. It can operate on aggregate published data or on individual patient data (IPD) contributed directly by study investigators. | 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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