방법 비교

	Nelson-Aalen 누적 위험 추정량	Cox 비례 위험 회귀분석	생존 곡선 비교를 위한 로그-순위 검정
분야	생존분석	생존분석	생존분석
계열	Survival analysis	Survival analysis	Survival analysis
기원 연도	1972	1972	1966
창시자	Wayne Nelson & Odd Aalen	Cox, D. R.	Mantel, N.
유형	Non-parametric cumulative hazard estimator	Semi-parametric hazard regression model	Non-parametric hypothesis test
원전	Nelson, W. (1972). Theory and applications of hazard plotting for censored failure data. Technometrics, 14(4), 945–966.	Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 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.
별칭	Nelson-Aalen cumulative hazard, Aalen estimator, empirical cumulative hazard, Nelson-Aalen kümülatif hazard tahmincisi	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu	Mantel log-rank test, Mantel-Cox test, log-rank sağkalım testi, Log-Rank Testi
관련	5	3	2
요약	The Nelson-Aalen estimator is a non-parametric estimator of the cumulative hazard function from right-censored time-to-event data. Developed by Wayne Nelson for reliability hazard plotting in 1972 and placed on a rigorous counting-process foundation by Odd Aalen in 1978, it accumulates the ratio of observed events to the number at risk at each event time, providing the natural hazard-scale companion to the Kaplan-Meier survival curve.	Cox proportional hazards regression, introduced by D. R. Cox in 1972, is a semi-parametric model that estimates how one or more covariates affect the hazard — the instantaneous rate of experiencing an event — while leaving the baseline hazard function unspecified. It is the standard multivariable method in survival analysis and produces hazard ratios that quantify the relative risk associated with each predictor.	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.