Comparer des méthodes

	Analyse de survie	Modélisation multiniveau
Domaine	Statistiques de recherche	Statistiques de recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1958	1992
Auteur d'origine	Edward L. Kaplan and Paul Meier	Anthony Bryk and Stephen Raudenbush
Type	Method	Method
Source fondatrice	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Alias	Kaplan-Meier analysis, Cox regression, TTE analysis	HLM, mixed-effects models, random effects models, MLM
Apparentées	3	3
Résumé	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.	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.