方法对比

	Cox比例风险模型	Event Data Analysis	生存分析
领域	流行病学	Political Science	研究统计学
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1972	—	1958
提出者	Sir David Roxbee Cox	Conflict-studies and computational-social-science traditions (McClelland, Schrodt, King)	Edward L. Kaplan and Paul Meier
类型	Semi-parametric regression model	Automated coding and analysis of who-did-what-to-whom event records	Method
开创性文献	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.	Schrodt, P. A. (2012). Precedents, Progress, and Prospects in Political Event Data. International Interactions, 38(4), 546–569.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
别名	Cox regression, Cox PH model, proportional hazards model, CPH	Event data coding, Political event data, Conflict event data, CAMEO event coding	Kaplan-Meier analysis, Cox regression, TTE analysis
相关	5	3	3
摘要	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.	Event data analysis converts streams of news reports into structured records of political interactions — who did what to whom, when — and aggregates them into time series of cooperation and conflict between actors. Each event is coded as a source actor, an action type drawn from an ontology such as CAMEO, a target actor, and a date. Modern systems extract these events automatically from millions of news stories, enabling near-real-time measurement of interstate and intrastate behavior for forecasting and analysis.	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.