Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Usajili wa Hatari za Uwiano wa Cox wa Vituo Vingi× | Uchanganuzi wa Kaplan-Meier× | |
|---|---|---|
| Nyanja | Epidemiolojia | Epidemiolojia |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1972 (Cox model); multicenter applications formalized 1980s–1990s | 1958 |
| Mwanzilishi≠ | D. R. Cox (Cox PH model); multicenter extension developed through collaborative trial methodology | Edward L. Kaplan and Paul Meier |
| Aina≠ | Semi-parametric survival regression for clustered data | Nonparametric survival estimator |
| Chanzo asilia≠ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Majina mbadala | multicenter Cox regression, multisite Cox PH model, stratified Cox model across centers, multicenter survival regression | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| Zinazohusiana≠ | 4 | 5 |
| Muhtasari≠ | Multicenter Cox proportional hazards regression extends the classic Cox PH model to studies conducted at two or more clinical sites or centers. It estimates the effect of predictors on time-to-event outcomes while explicitly accounting for clustering within centers, between-center heterogeneity, and potential differences in baseline hazard across sites. This design is standard practice in large multicenter RCTs and observational cohort studies in oncology, cardiology, and other clinical fields. | 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. |
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