Article (Scientific journals)
Bayesian proportional hazards model with time varying regression coefficients: a penalized Poisson regression approach
Lambert, Philippe; Eilers, Paul H.C.
2005In Statistics in Medicine, 24, p. 3977-3989
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Keywords :
Proportional hazards; P-splines; MCMC
Abstract :
[en] One can fruitfully approach survival problems without covariates in an actuarial way. In narrow time bins, the number of people at risk is counted together with the number of events. The relationship between time and probability of an event can then be estimated with a parametric or semi-parametric model. The number of events observed in each bin is described using a Poisson distribution with the log mean speci ed using a exible penalized B-splines model with a large number of equidistant knots. Regression on pertinent covariates can easily be performed using the same log-linear model, leading to the classical proportional hazard model. We propose to extend that model by allowing the regression coe cients to vary in a smooth way with time. Penalized B-splines models will be proposed for each of these coe cients. We show how the regression parameters and the penalty weights can be estimated e ciently using Bayesian inference tools based on the Metropolis-adjusted Langevin algorithm.
Disciplines :
Mathematics
Author, co-author :
Lambert, Philippe  ;  Université de Liège - ULiège > Institut des sciences humaines et sociales > Méthodes quantitatives en sciences sociales
Eilers, Paul H.C.
Language :
English
Title :
Bayesian proportional hazards model with time varying regression coefficients: a penalized Poisson regression approach
Publication date :
2005
Journal title :
Statistics in Medicine
ISSN :
0277-6715
eISSN :
1097-0258
Publisher :
John Wiley & Sons, Hoboken, United States - New Jersey
Volume :
24
Pages :
3977-3989
Peer reviewed :
Peer Reviewed verified by ORBi
Name of the research project :
IAP research network nr P5=24
Funders :
Belgian State (Federal Office for Scientific, Technical and Cultural Affairs
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