Compare methods

	Duration Models in Politics	Cox proportional hazards	Event Data Analysis	Survival Analysis
Field	Political Science	Epidemiology	Political Science	Research Statistics
Family	Regression model	Process / pipeline	Process / pipeline	Process / pipeline
Year of origin	1972	1972	—	1958
Originator	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)	Edward L. Kaplan and Paul Meier
Type	Time-to-event regression model	Semi-parametric regression model	Automated coding and analysis of who-did-what-to-whom event records	Method
Seminal source	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.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Aliases	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	Kaplan-Meier analysis, Cox regression, TTE analysis
Related	3	5	3	3
Summary	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.	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.