Sammenlign metoder

	Model for overlevende hændelser	Poisson- og negativ binomialregression
Fagområde	Overlevelsesanalyse	Økonometri
Familie	Survival analysis	Regression model
Oprindelsesår	1981	1998
Ophavsperson	Andersen & Gill (AG, 1982); Prentice, Williams & Peterson (PWP, 1981); Wei, Lin & Weissfeld (WLW, 1989)	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Type	Semi-parametric hazard model for repeated events	Generalized linear model for count data
Oprindelig kilde	Cook, R.J. & Lawless, J.F. (2007). The Statistical Analysis of Recurrent Events. Springer.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Aliasser	Tekrarlayan Olay Modeli (Recurrent Events), Andersen-Gill model, AG model, Wei-Lin-Weissfeld model	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Relaterede	4	4
Resumé	A recurrent event model is a survival analysis extension, formalised through the landmark contributions of Prentice, Williams and Peterson (1981), Andersen and Gill (1982), and Wei, Lin and Weissfeld (1989), that models time-to-event data when the same event — such as a hospital readmission, disease relapse, or equipment failure — can occur multiple times in the same individual. The three principal frameworks are the Andersen-Gill (AG) model, the Prentice-Williams-Peterson (PWP) stratified model, and the Wei-Lin-Weissfeld (WLW) marginal model, each making different assumptions about within-subject dependence.	Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred.