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| Matched Kaplan-Meier Analysis× | Kohorttitutkimus, jossa on täsmäytys (Matched Cohort Study)× | |
|---|---|---|
| Tieteenala | Epidemiologia | Epidemiologia |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1958 (KM); matched application formalized 1980s–2000s | Mid-20th century; propensity-score variant 1983 |
| Kehittäjä≠ | Kaplan & Meier (KM method, 1958); matching extensions developed through propensity score methods (Rosenbaum & Rubin, 1983) | Established practice; propensity-score matching formalized by Rosenbaum & Rubin (1983) |
| Tyyppi≠ | Nonparametric survival analysis with observational confounder control | Observational analytic study design |
| Alkuperäislähde≠ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457-481. DOI ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| Rinnakkaisnimet | KM analysis in matched cohorts, propensity-matched survival curves, matched survival analysis, paired Kaplan-Meier | matched follow-up study, paired cohort study, propensity-matched cohort, matched prospective study |
| Liittyvät≠ | 6 | 5 |
| Tiivistelmä≠ | Matched Kaplan-Meier analysis estimates and compares survival functions in groups that have been pre-balanced through individual or propensity-score matching. By applying the Kaplan-Meier product-limit estimator to matched cohorts or matched pairs, investigators can visualize time-to-event outcomes while controlling for confounders that would otherwise distort treatment or exposure comparisons in observational data. | A matched cohort study is an observational design in which each exposed participant is paired with one or more unexposed counterparts who share key characteristics — such as age, sex, or comorbidity status — before both groups are followed forward in time to compare incident outcomes. Matching controls for measured confounders at the design stage, reducing bias that would otherwise require statistical adjustment alone. |
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