Σύγκριση μεθόδων

	Ανάλυση Επιβίωσης	Λογιστική Παλινδρόμηση	Πολυεπίπεδη Μοντελοποίηση
Πεδίο	Ερευνητική Στατιστική	Ερευνητική Στατιστική	Ερευνητική Στατιστική
Οικογένεια	Process / pipeline	Process / pipeline	Process / pipeline
Έτος προέλευσης	1958	1958	1992
Δημιουργός	Edward L. Kaplan and Paul Meier	David Roxbee Cox	Anthony Bryk and Stephen Raudenbush
Τύπος	Method	Method	Method
Θεμελιώδης πηγή	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Εναλλακτικές ονομασίες	Kaplan-Meier analysis, Cox regression, TTE analysis	logit model, binomial logistic regression, LR	HLM, mixed-effects models, random effects models, MLM
Συναφείς	3	3	3
Σύνοψη	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.	Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.