手法を比較

	カプラン・マイヤー推定量	Cox Proportional Hazards	生存曲線比較のためのログランク検定
分野	統計学	疫学	生存時間解析
系統	Survival analysis	Process / pipeline	Survival analysis
提唱年	1958	1972	1966
提唱者	Edward L. Kaplan and Paul Meier	Sir David Roxbee Cox	Mantel, N.
種類	Nonparametric estimator	Semi-parametric regression model	Non-parametric hypothesis test
原典	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.	Mantel, N. (1966). Evaluation of Survival Data and Two New Rank Order Statistics Arising in Its Consideration. Cancer Chemotherapy Reports, 50(3), 163–170.
別名	KM estimator, product-limit estimator, Kaplan-Meier curve, survival curve estimator	Cox regression, Cox PH model, proportional hazards model, CPH	Mantel log-rank test, Mantel-Cox test, log-rank sağkalım testi, Log-Rank Testi
関連	2	5	2
概要	The Kaplan-Meier estimator is a nonparametric method for estimating the survival function S(t) — the probability that an individual survives beyond time t — from data that include censored observations. Introduced by Edward L. Kaplan and Paul Meier in their landmark 1958 JASA paper, it is the standard first step in any survival analysis and is among the most-cited statistical methods in biomedical research.	The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research.	The log-rank test, developed by Nathan Mantel in 1966, is a non-parametric hypothesis test that compares the overall survival experience of two or more groups throughout the entire follow-up period. It is the standard companion to Kaplan-Meier curves and determines whether observed differences between curves are statistically meaningful.