Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Izdzīvošanas analīze× | Logistiskā regresija× | |
|---|---|---|
| Nozare | Pētniecības statistika | Pētniecības statistika |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads | 1958 | 1958 |
| Autors≠ | Edward L. Kaplan and Paul Meier | David Roxbee Cox |
| Tips | Method | Method |
| Pirmavots≠ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Citi nosaukumi | Kaplan-Meier analysis, Cox regression, TTE analysis | logit model, binomial logistic regression, LR |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | 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. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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