Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Analiza de Supraviețuire cu Eșantioane Potrivite× | Estimator Kaplan-Meier× | |
|---|---|---|
| Domeniu≠ | Epidemiologie | Statistică |
| Familie≠ | Process / pipeline | Survival analysis |
| Anul apariției≠ | 1983 (propensity-score matching); applied to survival outcomes throughout 1990s–2000s | 1958 |
| Autorul original≠ | Building on Kaplan & Meier (1958) and Cox (1972); matching framework formalised in observational study design literature (Rosenbaum & Rubin, 1983) | Edward L. Kaplan and Paul Meier |
| Tip≠ | Observational study analytic method | Nonparametric estimator |
| Sursa seminală≠ | Austin, P. C. (2014). Graphical assessments of the balance of propensity score matched samples: A SAS macro. Journal of Statistical Software, 58(7), 1-29. Also see Austin, P. C. (2017). A tutorial on multilevel survival analysis: Methods, models and applications. International Statistical Review, 85(2), 185-203. link ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Denumiri alternative | matched time-to-event analysis, propensity-matched survival analysis, matched Kaplan-Meier analysis, paired survival analysis | KM estimator, product-limit estimator, Kaplan-Meier curve, survival curve estimator |
| Înrudite≠ | 4 | 2 |
| Rezumat≠ | Matched survival analysis combines a matching design — typically propensity score matching or exact matching on key covariates — with time-to-event methods such as Kaplan-Meier estimation and the Cox proportional hazards model. By pairing treated and control subjects who are similar on observed confounders before estimating survival curves or hazard ratios, the approach reduces confounding bias in non-randomised studies and produces more credible comparisons of event-free survival between exposure groups. | The Kaplan-Meier estimator is a nonparametric method for estimating the survival function S(t) — the probability that an individual survives beyond time t — from data that include censored observations. Introduced by Edward L. Kaplan and Paul Meier in their landmark 1958 JASA paper, it is the standard first step in any survival analysis and is among the most-cited statistical methods in biomedical research. |
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