Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchanganuzi wa Unusurika wa Matarajio× | Uchambuzi wa Uhai× | |
|---|---|---|
| Nyanja≠ | Epidemiolojia | Takwimu za Utafiti |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1958–1972 (foundational methods); prospective design emphasis formalized by 1980s | 1958 |
| Mwanzilishi≠ | Kaplan & Meier (estimator, 1958); Cox (proportional hazards model, 1972); prospective design formalised in modern clinical epidemiology | Edward L. Kaplan and Paul Meier |
| Aina≠ | Longitudinal observational or experimental study design with time-to-event analysis | Method |
| Chanzo asilia≠ | Kleinbaum, D. G., & Klein, M. (2012). Survival Analysis: A Self-Learning Text (3rd ed.). Springer. ISBN: 978-1441966452 | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Majina mbadala≠ | prospective time-to-event analysis, prospective failure-time analysis, forward-looking survival study, prospective event-time study | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Zinazohusiana≠ | 5 | 3 |
| Muhtasari≠ | Prospective survival analysis is a longitudinal study design in which participants are enrolled before the event of interest occurs, followed forward in time under standardised conditions, and analysed using survival-analytic methods to estimate the time until a defined clinical endpoint — such as death, disease recurrence, or treatment failure. Because data are collected prospectively, exposure and covariate information are recorded before outcomes are known, substantially reducing recall and selection bias relative to retrospective approaches. | 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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