Comparar métodos

	Regressão Logística	Análise de Sobrevida
Área	Estatística para pesquisa	Estatística para pesquisa
Família	Process / pipeline	Process / pipeline
Ano de origem	1958	1958
Autor original	David Roxbee Cox	Edward L. Kaplan and Paul Meier
Tipo	Method	Method
Fonte seminal	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Outros nomes	logit model, binomial logistic regression, LR	Kaplan-Meier analysis, Cox regression, TTE analysis
Relacionados	3	3
Resumo	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.	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.