Salīdzināt metodes

	Risk-Adjusted Survival Analysis	Izdzīvošanas analīze
Nozare	Epidemioloģija	Pētniecības statistika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1972 (Cox regression); broader covariate-adjusted survival methods developed 1970s–1990s	1958
Autors	D. R. Cox (regression framework); extensions via Kaplan & Meier, Breslow, and others	Edward L. Kaplan and Paul Meier
Tips	Observational and experimental analytical method	Method
Pirmavots	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Citi nosaukumi	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
Saistītās	5	3
Kopsavilkums	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.