Comparer des méthodes

	Duration Models in Politics	Cox proportional hazards	Event Data Analysis
Domaine	Political Science	Épidémiologie	Political Science
Famille	Regression model	Process / pipeline	Process / pipeline
Année d'origine	1972	1972	—
Auteur d'origine	David R. Cox (Cox model); popularized in political science by Janet Box-Steffensmeier & Bradford Jones	Sir David Roxbee Cox	Conflict-studies and computational-social-science traditions (McClelland, Schrodt, King)
Type	Time-to-event regression model	Semi-parametric regression model	Automated coding and analysis of who-did-what-to-whom event records
Source fondatrice	Box-Steffensmeier, J. M., & Jones, B. S. (2004). Event History Modeling: A Guide for Social Scientists. Cambridge University Press. ISBN: 9780521546737	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	Event history models, Survival models in political science, Hazard models, Time-to-event models in politics	Cox regression, Cox PH model, proportional hazards model, CPH	Event data coding, Political event data, Conflict event data, CAMEO event coding
Apparentées	3	5	3
Résumé	Duration models — also called event history or survival models — analyze the time until a political event occurs: how long a cabinet lasts before it falls, how long a war runs before it ends, how long a policy takes to be adopted, or how long a regime survives. Rather than asking only whether an event happens, these models ask when, modeling the hazard rate as a function of covariates while correctly handling censored cases that have not yet experienced the event. The Cox proportional hazards model and parametric alternatives such as the Weibull, popularized in political science by Box-Steffensmeier and Jones, form the core toolkit.	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.