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	Duration Models in Politics	Model Coxa z proporcjonalnym hazardem	Analiza przeżycia
Dziedzina	Political Science	Epidemiologia	Statystyka w badaniach
Rodzina	Regression model	Process / pipeline	Process / pipeline
Rok powstania	1972	1972	1958
Twórca	David R. Cox (Cox model); popularized in political science by Janet Box-Steffensmeier & Bradford Jones	Sir David Roxbee Cox	Edward L. Kaplan and Paul Meier
Typ	Time-to-event regression model	Semi-parametric regression model	Method
Źródło pierwotne	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.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Inne nazwy	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	Kaplan-Meier analysis, Cox regression, TTE analysis
Pokrewne	3	5	3
Podsumowanie	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.	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.