Lifetime Data Anal - A proportional hazards regression model with change-points in the baseline function.

Tópicos

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Resumo

In this article, we consider a new regression model for counting processes under a proportional hazards assumption. This model is motivated by the need of understanding the evolution of the booking process of a railway company. The main novelty of the approach consists in assuming that the baseline hazard function is piecewise constant, with unknown times of jump (these times of jump are estimated from the data as model parameters). Hence, the parameters of the model can be separated into two different types: parameters that measure the influence of the covariates, and parameters from a multiple change-point model for the baseline. Cox's semiparametric regression can be seen as a limit case of our model. We develop an iterative procedure to estimate the different parameters, and a test procedure that allows to perform change-point detection in the baseline. Our technique is supported by simulation studies and a real data analysis, which show that our model can be a reasonable alternative to Cox's regression model, particularly in the presence of tied event times.

Resumo Limpo

articl consid new regress model count process proport hazard assumpt model motiv need understand evolut book process railway compani main novelti approach consist assum baselin hazard function piecewis constant unknown time jump time jump estim data model paramet henc paramet model can separ two differ type paramet measur influenc covari paramet multipl changepoint model baselin cox semiparametr regress can seen limit case model develop iter procedur estim differ paramet test procedur allow perform changepoint detect baselin techniqu support simul studi real data analysi show model can reason altern cox regress model particular presenc tie event time

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