invGauss: Threshold regression that fits the (randomized drift) inverse Gaussian distribution to survival data. invGauss fits the (randomized drift) inverse Gaussian distribution to survival data. The model is described in Aalen OO, Borgan O, Gjessing HK. Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based on describing time to event as the barrier hitting time of a Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution. The model allows covariates to influence starting values of the Wiener process and/or average drift towards a barrier, with a user-defined choice of link functions.

References in zbMATH (referenced in 60 articles , 1 standard article )

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  1. Kayid, M.; Izadkhah, S.; Zuo, Ming J.: Some results on the relative ordering of two frailty models (2017)
  2. Unkel, Steffen: On the conditional probability for assessing time dependence of association in shared frailty models with bivariate current status data (2017)
  3. Unkel, Steffen: On the shape of the cross-ratio function in bivariate survival models induced by truncated and folded normal frailty distributions (2017)
  4. Cortese, Giuliana; Sartori, Nicola: Integrated likelihoods in parametric survival models for highly clustered censored data (2016)
  5. Gilardoni, Gustavo L.; Guerra de Toledo, Maria Luiza; Freitas, Marta A.; Colosimo, Enrico A.: Dynamics of an optimal maintenance policy for imperfect repair models (2016)
  6. Rocha, Ricardo; Nadarajah, Saralees; Tomazella, Vera; Louzada, Francisco: Two new defective distributions based on the Marshall-Olkin extension (2016)
  7. Saarela, Olli: A case-base sampling method for estimating recurrent event intensities (2016)
  8. Yang, Hanfang; Liu, Shen; Zhao, Yichuan: Jackknife empirical likelihood for linear transformation models with right censoring (2016)
  9. Aalen, Odd O.; Cook, Richard J.; Røysland, Kjetil: Does Cox analysis of a randomized survival study yield a causal treatment effect? (2015)
  10. Borgan, Ørnulf; Keogh, Ruth: Nested case-control studies: should one break the matching? (2015)
  11. Breslow, Norman E.; Hu, Jie; Wellner, Jon A.: Z-estimation and stratified samples: application to survival models (2015)
  12. Erich, Roger; Pennell, Michael L.: Ornstein-Uhlenbeck threshold regression for time-to-event data with and without a cure fraction (2015)
  13. Lawless, J.F.; Rad, N.Nazeri: Estimation and assessment of Markov multistate models with intermittent observations on individuals (2015)
  14. Li, Xiaohu; Fang, Rui: Ordering properties of order statistics from random variables of Archimedean copulas with applications (2015)
  15. Brinks, Ralph; Landwehr, Sandra: Age- and time-dependent model of the prevalence of non-communicable diseases and application to dementia in Germany (2014)
  16. Enki, Doyo G.; Noufaily, Angela; Farrington, C.Paddy: A time-varying shared frailty model with application to infectious diseases (2014)
  17. Farewell, Vernon T.; Tom, Brian D.M.: The versatility of multi-state models for the analysis of longitudinal data with unobservable features (2014)
  18. Tattar, Prabhanjan N.; Vaman, H.J.: The $k$-sample problem in a multi-state model and testing transition probability matrices (2014)
  19. Baraldo, Stefano; Ieva, Francesca; Paganoni, Anna Maria; Vitelli, Valeria: Outcome prediction for heart failure telemonitoring via generalized linear models with functional covariates (2013)
  20. Bijwaard, Govert E.; Ridder, Geert; Woutersen, Tiemen: A simple GMM estimator for the semiparametric mixed proportional hazard model (2013)

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