Predicting and Modelling of Survival Data when Cox's Regression Model does not hold

Thomas H. Scheike; Mei-Jie Zhang

Predicting and Modelling of Survival Data when Cox's Regression Model does not hold

Section of Biostatistics

Abstract

Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects

Original language	English
Place of Publication	Biostatistisk Afdeling, Københavns Universitet
Pages	26
Publication status	Published - 2002

Cite this

@techreport{679df6b074c011dbbee902004c4f4f50,

title = "Predicting and Modelling of Survival Data when Cox's Regression Model does not hold",

abstract = "Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects",

author = "Scheike, {Thomas H.} and Mei-Jie Zhang",

year = "2002",

language = "English",

pages = "26",

type = "WorkingPaper",

}

TY - UNPB

T1 - Predicting and Modelling of Survival Data when Cox's Regression Model does not hold

AU - Scheike, Thomas H.

AU - Zhang, Mei-Jie

PY - 2002

Y1 - 2002

N2 - Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects

AB - Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects

M3 - Working paper

SP - 26

BT - Predicting and Modelling of Survival Data when Cox's Regression Model does not hold

CY - Biostatistisk Afdeling, Københavns Universitet

ER -