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

Institut National de la Santé et de la Recherche Médicale, U875 (Biostatistique), Bordeaux, F-33076, France Bordeaux, F-33076, France.
Biostatistics (Impact Factor: 2.24). 11/2007; 8(4):708-21. DOI: 10.1093/biostatistics/kxl043
Source: PubMed

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 modeling for 2 survival processes has received considerable attention because it
makes it possible to study the joint evolution over time of 2 processes and gives unbiased and efficient parameters. The most
commonly used estimation procedure in the joint models for survival events is the expectation maximization 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 semiparametric
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 modeled with death.

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Available from: Virginie Rondeau, Jul 04, 2015