Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchambuzi Ulioboreshwa wa Hatari Zinazoshindana× | Uchambuzi wa Uhai× | |
|---|---|---|
| Nyanja≠ | Epidemiolojia | Takwimu za Utafiti |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1999 (foundational Fine-Gray model); adaptive extensions 2000s–2010s | 1958 |
| Mwanzilishi≠ | Fine & Gray (subdistribution hazard, 1999); adaptive extensions by Beyersmann, Schumacher and colleagues | Edward L. Kaplan and Paul Meier |
| Aina≠ | Statistical survival analysis with adaptive interim monitoring | Method |
| Chanzo asilia≠ | Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509. 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≠ | adaptive Fine-Gray analysis, group-sequential competing risks, adaptive subdistribution hazard analysis, competing risks adaptive design | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Zinazohusiana≠ | 2 | 3 |
| Muhtasari≠ | Adaptive competing risks analysis combines the Fine-Gray subdistribution hazard framework — which models the cumulative incidence of one cause of failure in the presence of other mutually exclusive causes — with adaptive or group-sequential interim monitoring rules. This allows a clinical trial or observational study to be modified mid-course (e.g., sample size reassessment, early stopping) based on accumulating competing-risk data while maintaining pre-specified type I error control. | 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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