Comparar métodos

	Modelo de Riesgos Proporcionales de Cox	Event Data Analysis
Campo	Epidemiología	Political Science
Familia	Process / pipeline	Process / pipeline
Año de origen	1972	—
Autor original	Sir David Roxbee Cox	Conflict-studies and computational-social-science traditions (McClelland, Schrodt, King)
Tipo	Semi-parametric regression model	Automated coding and analysis of who-did-what-to-whom event records
Fuente seminal	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.
Alias	Cox regression, Cox PH model, proportional hazards model, CPH	Event data coding, Political event data, Conflict event data, CAMEO event coding
Relacionados	5	3
Resumen	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.