手法を比較

	Prospective Cox Proportional Hazards	Kaplan-Meier Analysis
分野	疫学	疫学
系統	Process / pipeline	Process / pipeline
提唱年	1972 (Cox model); widespread prospective application from late 1970s	1958
提唱者	David R. Cox (model); applied prospectively in large cohort studies from 1970s onward	Edward L. Kaplan and Paul Meier
種類	Semi-parametric survival regression applied to prospectively collected time-to-event data	Nonparametric survival estimator
原典	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
別名	prospective Cox regression, Cox PH prospective study, prospective survival regression, prospective hazard modeling	KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve
関連	4	5
概要	Prospective Cox proportional hazards regression combines a forward-looking cohort design — in which participants are enrolled before outcomes occur and followed over time — with Cox's semi-parametric survival model. The method estimates how baseline covariates measured at enrollment influence the rate at which participants experience a time-to-event outcome, while preserving the temporal direction required for causal inference. It is one of the most widely used analytical frameworks in clinical epidemiology and chronic disease research.	Kaplan-Meier (KM) analysis is a nonparametric method for estimating the survival function from time-to-event data. Introduced by Kaplan and Meier in 1958, it produces the classic step-function survival curve that shows the probability of surviving beyond each observed event time, correctly accounting for censored observations — participants who left the study or had not yet experienced the event by the end of follow-up. It is one of the most widely used techniques in clinical and epidemiological research.