invGauss

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 68 articles , 1 standard article )

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  1. Jiang, Fei; Haneuse, Sebastien: A semi-parametric transformation frailty model for semi-competing risks survival data (2017)
  2. Kayid, M.; Izadkhah, S.; Zuo, Ming J.: Some results on the relative ordering of two frailty models (2017)
  3. Leão, Jeremias; Leiva, Víctor; Saulo, Helton; Tomazella, Vera: Birnbaum-Saunders frailty regression models: diagnostics and application to medical data (2017)
  4. Lindholm, Mathias: A note on the connection between some classical mortality laws and proportional frailty (2017)
  5. Unkel, Steffen: On the shape of the cross-ratio function in bivariate survival models induced by truncated and folded normal frailty distributions (2017)
  6. Unkel, Steffen: On the conditional probability for assessing time dependence of association in shared frailty models with bivariate current status data (2017)
  7. Cortese, Giuliana; Sartori, Nicola: Integrated likelihoods in parametric survival models for highly clustered censored data (2016)
  8. 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)
  9. Grafféo, Nathalie; Castell, Fabienne; Belot, Aurélien; Giorgi, Roch: A log-rank-type test to compare net survival distributions (2016)
  10. Rocha, Ricardo; Nadarajah, Saralees; Tomazella, Vera; Louzada, Francisco: Two new defective distributions based on the Marshall-Olkin extension (2016)
  11. Saarela, Olli: A case-base sampling method for estimating recurrent event intensities (2016)
  12. Yang, Hanfang; Liu, Shen; Zhao, Yichuan: Jackknife empirical likelihood for linear transformation models with right censoring (2016)
  13. Aalen, Odd O.; Cook, Richard J.; Røysland, Kjetil: Does Cox analysis of a randomized survival study yield a causal treatment effect? (2015)
  14. Borgan, Ørnulf; Keogh, Ruth: Nested case-control studies: should one break the matching? (2015)
  15. Borgan, Ørnulf; Zhang, Ying: Using cumulative sums of martingale residuals for model checking in nested case-control studies (2015)
  16. Breslow, Norman E.; Hu, Jie; Wellner, Jon A.: Z-estimation and stratified samples: application to survival models (2015)
  17. Cécilia-Joseph, Elsa; Auvert, Bertran; Broët, Philippe; Moreau, Thierry: Influence of trial duration on the bias of the estimated treatment effect in clinical trials when individual heterogeneity is ignored (2015)
  18. Erich, Roger; Pennell, Michael L.: Ornstein-Uhlenbeck threshold regression for time-to-event data with and without a cure fraction (2015)
  19. Lawless, J.F.; Rad, N.Nazeri: Estimation and assessment of Markov multistate models with intermittent observations on individuals (2015)
  20. Li, Xiaohu; Fang, Rui: Ordering properties of order statistics from random variables of Archimedean copulas with applications (2015)

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