parfm: Parametric Frailty Models in R. Frailty models are getting more and more popular to account for overdispersion and/or clustering in survival data. When the form of the baseline hazard is somehow known in advance, the parametric estimation approach can be used advantageously. Nonetheless, there is no unified widely available software that deals with the parametric frailty model. The new parfm package remedies that lack by providing a wide range of parametric frailty models in R. The gamma, inverse Gaussian, and positive stable frailty distributions can be specified, together with five different baseline hazards. Parameter estimation is done by maximising the marginal log-likelihood, with right-censored and possibly left-truncated data. In the multivariate setting, the inverse Gaussian may encounter numerical difficulties with a huge number of events in at least one cluster. The positive stable model shows analogous difficulties but an ad-hoc solution is implemented, whereas the gamma model is very resistant due to the simplicity of its Laplace transform.

References in zbMATH (referenced in 12 articles , 1 standard article )

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  1. Hadrien Charvat, Aurelien Belot: mexhaz: An R Package for Fitting Flexible Hazard-Based Regression Models for Overall and Excess Mortality with a Random Effect (2021) not zbMATH
  2. Barreto-Souza, Wagner; Mayrink, Vinícius Diniz: Semiparametric generalized exponential frailty model for clustered survival data (2019)
  3. Keiding, Niels; Albertsen, Katrine Lykke; Rytgaard, Helene Charlotte; Sørensen, Anne Lyngholm: Prevalent cohort studies and unobserved heterogeneity (2019)
  4. Theodor Balan; Hein Putter: frailtyEM: An R Package for Estimating Semiparametric Shared Frailty Models (2019) not zbMATH
  5. John Monaco; Malka Gorfine; Li Hsu: General Semiparametric Shared Frailty Model: Estimation and Simulation with frailtySurv (2018) not zbMATH
  6. Raíces, Ivette; Sistachs, Vivian; Liero, Hannelore; Yera, Isis; Martínez, Liset: Analysis of parametric frailty models to estimate the risk of amputation (2018)
  7. Texier, Matthieu; Rotolo, Federico; Ducreux, Michel; Bouché, Olivier; Pignon, Jean-Pierre; Michiels, Stefan: Evaluation of treatment effect with paired failure times in a single-arm phase II trial in oncology (2018)
  8. Ytterstad, Elinor: Frailty in survival analysis of widowhood mortality (2018)
  9. Feng, Cindy X.; Rostami, Mehdi; Li, Longhai: Impact of misspecified residual correlation structure on the parameter estimates in a shared spatial frailty model (2017)
  10. John V. Monaco, Malka Gorfine, Li Hsu: General Semiparametric Shared Frailty Model Estimation and Simulation with frailtySurv (2017) arXiv
  11. Cortese, Giuliana; Sartori, Nicola: Integrated likelihoods in parametric survival models for highly clustered censored data (2016)
  12. Marco Munda; Federico Rotolo; Catherine Legrand: parfm: Parametric Frailty Models in R (2012) not zbMATH