This article focuses on shared frailty models for correlated failure times, as well as joint frailty models for the simultaneous analysis of recurrent events (eg, appearance of new cancerous lesions or hospital readmissions) and a major terminal event (typically, death). As extensions of the Cox model, these joint models usually assume a frailty proportional hazards model for each of the recurrent and terminal event processes. In order to extend these models beyond the proportional hazards assumption, our proposal is to replace these proportional hazards models with generalized survival models, for which the survival function is modeled as a linear predictor through a link function. Depending on the link function considered, these can be reduced to proportional hazards, proportional odds, additive hazards, or probit models. We first consider a fully parametric framework for the time and covariate effects. For proportional and additive hazards models, our approach also allows the use of smooth functions for baseline hazard functions and time-varying coefficients. The dependence between recurrent and terminal event processes is modeled by conditioning on a shared frailty acting differently on the two processes. Parameter estimates are provided using the maximum (penalized) likelihood method, implemented in the R package frailtypack (function GenfrailtyPenal). We perform simulation studies to assess the method, which is also illustrated on real datasets.< Réduire