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 119 articles , 1 standard article )

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  1. Blanche, Paul: Confidence intervals for the cumulative incidence function via constrained NPMLE (2020)
  2. Botosaru, Irene: Nonparametric analysis of a duration model with stochastic unobserved heterogeneity (2020)
  3. Chowdhury, Rafiqul I.; Islam, M. Ataharul: Prediction of risks of sequence of events using multistage proportional hazards model: a marginal-conditional modelling approach (2020)
  4. Cook, Richard J.: Book review of: R. L. Prentice and S. Zhao, The statistical analysis of multivariate failure time data. A marginal modeling approach. (2020)
  5. Deresa, Negera Wakgari; Van Keilegom, Ingrid: A multivariate normal regression model for survival data subject to different types of dependent censoring (2020)
  6. Fang, Guanhua; Ying, Zhiliang: Latent theme dictionary model for finding co-occurrent patterns in process data (2020)
  7. Feifel, Jan; Gebauer, Madlen; Schumacher, Martin; Beyersmann, Jan: Nested exposure case-control sampling: a sampling scheme to analyze rare time-dependent exposures (2020)
  8. Fernández, Tamara; Rivera, Nicolás: Kaplan-Meier V- and U-statistics (2020)
  9. Fuino, Michel; Wagner, Joël: Duration of long-term care: socio-economic factors, type of care interactions and evolution (2020)
  10. Ha, Il Do; Xiang, Liming; Peng, Mengjiao; Jeong, Jong-Hyeon; Lee, Youngjo: Frailty modelling approaches for semi-competing risks data (2020)
  11. Hosni, Adil Imad Eddine; Li, Kan; Ahmad, Sadique: Minimizing rumor influence in multiplex online social networks based on human individual and social behaviors (2020)
  12. Ramchandani, Ritesh; Finkelstein, Dianne M.; Schoenfeld, David A.: Estimation for an accelerated failure time model with intermediate states as auxiliary information (2020)
  13. Bednarski, Tadeusz; Skolimowska-Kulig, Magdalena: On scale Fisher consistency of maximum likelihood estimator for the exponential regression model under arbitrary frailty (2019)
  14. Bluhmki, Tobias; Dobler, Dennis; Beyersmann, Jan; Pauly, Markus: The wild bootstrap for multivariate Nelson-Aalen estimators (2019)
  15. Borgan, Ørnulf; Gjessing, Håkon K.: Special issue dedicated to Odd O. Aalen (2019)
  16. Brown, Paul M.; Ezekowitz, Justin A.: Frailty modelling for multitype recurrent events in clinical trials (2019)
  17. Commenges, Daniel: Dealing with death when studying disease or physiological marker: the stochastic system approach to causality (2019)
  18. Didelez, Vanessa: Defining causal mediation with a longitudinal mediator and a survival outcome (2019)
  19. Furrer, Christian: Experience rating in the classic Markov chain life insurance setting (2019)
  20. Golzy, Mojgan; Carter, Randy L.: Generalized frailty models for analysis of recurrent events (2019)

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