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Joint frailty models for recurring events and death using maximum penalized likelihood estimation: application on cancer events

Abstract : The observation of repeated events for subjects in cohort studies
could be terminated by loss to follow-up, end-of-study, or a major
failure event such as death. In this context, the major failure
event could be correlated with recurrent events and the usual
assumption of noninformative censoring of the recurrent event
process by death, required by most statistical analyses, can
be violated. Recently joint modelling for two survival processes
has received considerable attention because it makes it
possible to study the joint evolution over time of two processes and gives unbiased and efficient parameters. The most commonly used estimation procedure in the joint models for survival events is the EM algorithm. We show how maximum penalized likelihood estimation can be applied to nonparametric estimation of the continuous hazard functions in a general joint frailty model with right censoring and delayed entry. The simulation study demonstrates that this semi-parametric approach yields satisfactory results in this complex setting. As an illustration, such an approach is applied to a prospective cohort with recurrent events of follicular lymphomas, jointly modelled with death.
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https://halshs.archives-ouvertes.fr/halshs-00121706
Contributor : Virginie Rondeau Connect in order to contact the contributor
Submitted on : Thursday, December 21, 2006 - 3:49:01 PM
Last modification on : Tuesday, April 19, 2022 - 10:16:52 AM
Long-term archiving on: : Thursday, September 20, 2012 - 4:32:51 PM

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  • HAL Id : halshs-00121706, version 1

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Virginie Rondeau, Simone Mathoulin-Pélissier, Hélène Jacqmin-Gadda, Véronique Brouste, Pierre Soubeyran. Joint frailty models for recurring events and death using maximum penalized likelihood estimation: application on cancer events. {date}. ⟨halshs-00121706⟩

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