Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchanganuzi wa Unusurika wenye Marekebisho ya Hatari× | Uchambuzi wa Uhai× | |
|---|---|---|
| Nyanja≠ | Epidemiolojia | Takwimu za Utafiti |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1972 (Cox regression); broader covariate-adjusted survival methods developed 1970s–1990s | 1958 |
| Mwanzilishi≠ | D. R. Cox (regression framework); extensions via Kaplan & Meier, Breslow, and others | Edward L. Kaplan and Paul Meier |
| Aina≠ | Observational and experimental analytical method | Method |
| Chanzo asilia≠ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220. link ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Majina mbadala≠ | covariate-adjusted survival analysis, adjusted time-to-event analysis, risk-stratified survival analysis, adjusted Kaplan-Meier / Cox analysis | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Zinazohusiana≠ | 5 | 3 |
| Muhtasari≠ | Risk-adjusted survival analysis estimates the time to an event of interest — such as death, relapse, or hospital readmission — while simultaneously accounting for baseline differences in patient characteristics (covariates). By incorporating confounders such as age, comorbidities, or disease severity, it produces hazard ratios, survival curves, and median survival estimates that are attributable to the factor of interest rather than to pre-existing risk differences between groups. | 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. |
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