Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Pragmatic Kaplan-Meier analysis× | Selviytymisanalyysi× | |
|---|---|---|
| Tieteenala≠ | Epidemiologia | Tutkimuksen tilastomenetelmät |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1958 (estimator); pragmatic application formalized 1967 onward | 1958 |
| Kehittäjä≠ | Kaplan & Meier (estimator, 1958); Schwartz & Lellouch (pragmatic trial framework, 1967) | Edward L. Kaplan and Paul Meier |
| Tyyppi≠ | Non-parametric survival estimator within pragmatic study design | Method |
| Alkuperäislähde | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Rinnakkaisnimet≠ | pragmatic KM analysis, real-world Kaplan-Meier, pragmatic survival curve estimation, KM analysis in pragmatic trials | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Liittyvät≠ | 5 | 3 |
| Tiivistelmä≠ | Pragmatic Kaplan-Meier analysis applies the non-parametric Kaplan-Meier product-limit estimator to time-to-event data collected under real-world or pragmatic conditions — diverse populations, routine clinical care, minimal exclusions, and standard-of-care comparators. Unlike explanatory trials designed to isolate a treatment effect under ideal conditions, pragmatic designs accept real-world heterogeneity, and the resulting survival curves reflect the effectiveness of an intervention as it actually performs in clinical practice. | Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters. |
| ScholarGateAineisto ↗ |
|
|