विधियों की तुलना करें

	पुनरावर्ती घटना उत्तरजीविता मॉडल	ऋणात्मक द्विपद समाश्रयण (Negative Binomial Regression)
क्षेत्र	उत्तरजीविता	अर्थमिति
परिवार	Survival analysis	Regression model
उद्भव वर्ष	1981	2011
प्रवर्तक	Andersen & Gill (AG, 1982); Prentice, Williams & Peterson (PWP, 1981); Wei, Lin & Weissfeld (WLW, 1989)	Hilbe (textbook treatment); generalized linear model framework
प्रकार	Semi-parametric hazard model for repeated events	Generalized linear model for count data
मौलिक स्रोत	Cook, R.J. & Lawless, J.F. (2007). The Statistical Analysis of Recurrent Events. Springer.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
उपनाम	Tekrarlayan Olay Modeli (Recurrent Events), Andersen-Gill model, AG model, Wei-Lin-Weissfeld model	NB regression, NB2 regression, negatif binom regresyonu
संबंधित	4	4
सारांश	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.	Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.