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.

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  1. Afzal, Arfan Raheen; Yang, Jing; Lu, Xuewen: Variable selection in partially linear additive hazards model with grouped covariates and a diverging number of parameters (2021)
  2. Bednarski, Tadeusz; Nowak, Piotr B.: Scaled Fisher consistency of the partial likelihood estimator in the Cox model with arbitrary frailty (2021)
  3. Dempsey, Walter: Exchangeable Markov multi-state survival processes (2021)
  4. Li, Xiao-Yang; Ye, Zhi-Sheng; Tang, Cheng Yong: Estimating the inter-occurrence time distribution from superposed renewal processes (2021)
  5. Ma, Chuoxin; Dai, Hongsheng; Pan, Jianxin: Modeling past event feedback through biomarker dynamics in the multistate event analysis for cardiovascular disease data (2021)
  6. Maltzahn, Niklas; Hoff, Rune; Aalen, Odd O.; Mehlum, Ingrid S.; Putter, Hein; Gran, Jon Michael: A hybrid landmark Aalen-Johansen estimator for transition probabilities in partially non-Markov multi-state models (2021)
  7. Blanche, Paul: Confidence intervals for the cumulative incidence function via constrained NPMLE (2020)
  8. Botosaru, Irene: Nonparametric analysis of a duration model with stochastic unobserved heterogeneity (2020)
  9. Chowdhury, Rafiqul I.; Islam, M. Ataharul: Prediction of risks of sequence of events using multistage proportional hazards model: a marginal-conditional modelling approach (2020)
  10. 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)
  11. Deresa, Negera Wakgari; Van Keilegom, Ingrid: A multivariate normal regression model for survival data subject to different types of dependent censoring (2020)
  12. Fang, Guanhua; Ying, Zhiliang: Latent theme dictionary model for finding co-occurrent patterns in process data (2020)
  13. Feifel, Jan; Gebauer, Madlen; Schumacher, Martin; Beyersmann, Jan: Nested exposure case-control sampling: a sampling scheme to analyze rare time-dependent exposures (2020)
  14. Fernández, Tamara; Rivera, Nicolás: Kaplan-Meier V- and U-statistics (2020)
  15. Fuino, Michel; Wagner, Joël: Duration of long-term care: socio-economic factors, type of care interactions and evolution (2020)
  16. Ha, Il Do; Xiang, Liming; Peng, Mengjiao; Jeong, Jong-Hyeon; Lee, Youngjo: Frailty modelling approaches for semi-competing risks data (2020)
  17. Hosni, Adil Imad Eddine; Li, Kan; Ahmad, Sadique: Minimizing rumor influence in multiplex online social networks based on human individual and social behaviors (2020)
  18. Mansourvar, Zahra; Asadi, Majid: An extension of the Cox-Czanner divergence measure to residual lifetime distributions with applications (2020)
  19. Ramchandani, Ritesh; Finkelstein, Dianne M.; Schoenfeld, David A.: Estimation for an accelerated failure time model with intermediate states as auxiliary information (2020)
  20. Schulz, Jörn; Kvaløy, Jan Terje; Engan, Kjersti; Eftestøl, Trygve; Jatosh, Samwel; Kidanto, Hussein; Ersdal, Hege: State transition modeling of complex monitored health data (2020)

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