R package timereg: Flexible regression models for survival data. Programs for Martinussen and Scheike (2006), ‘Dynamic Regression Models for Survival Data’, Springer Verlag. Plus more recent developments. Additive survival model, semiparametric proportional odds model, fast cumulative residuals, excess risk models and more. Flexible competing risks regression including GOF-tests. Two-stage frailty modelling. PLS for the additive risk model. Lasso in ahaz package.

References in zbMATH (referenced in 56 articles , 1 standard article )

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  1. Chen, Chyong-Mei; Shen, Pao-Sheng: Conditional maximum likelihood estimation in semiparametric transformation model with LTRC data (2018)
  2. Liu, Wanrong; Fang, Jianglin; Lu, Xuewen: Additive-multiplicative hazards model with current status data (2018)
  3. Milhaud, Xavier; Dutang, Christophe: Lapse tables for lapse risk management in insurance: a competing risk approach (2018)
  4. Friedrich, Sarah; Konietschke, Frank; Pauly, Markus: A wild bootstrap approach for nonparametric repeated measurements (2017)
  5. Gallardo, Diego I.; Gómez, Yolanda M.; Arnold, Barry C.; Gómez, Héctor W.: The Pareto IV power series cure rate model with applications (2017)
  6. Han, Dongxiao; Sun, Liuquan; Sun, Yanqing; Qi, Li: Mark-specific additive hazards regression with continuous marks (2017)
  7. Lv, Xiaofeng; Zhang, Gupeng; Ren, Guangyu: Gini index estimation for lifetime data (2017)
  8. Mansourvar, Zahra; Martinussen, Torben: Estimation of average causal effect using the restricted mean residual lifetime as effect measure (2017)
  9. Moradian, Hoora; Larocque, Denis; Bellavance, François: $L_1$ splitting rules in survival forests (2017)
  10. Pal, Suvra; Balakrishnan, N.: Likelihood inference for the destructive exponentially weighted Poisson cure rate model with Weibull lifetime and an application to melanoma data (2017)
  11. Suzuki, Adriano K.; Barriga, Gladys D. C.; Louzada, Francisco; Cancho, Vicente G.: A general long-term aging model with different underlying activation mechanisms: modeling, Bayesian estimation, and case influence diagnostics (2017)
  12. Wilson, Kevin J.; Farrow, Malcolm: Bayes linear kinematics in a dynamic survival model (2017)
  13. Boruvka, Audrey; Takahara, Glen; Tu, Dongsheng: Data-driven ridge regression for Aalen’s additive risk model (2016)
  14. Cordeiro, Gauss M.; Cancho, Vicente G.; Ortega, Edwin M. M.; Barriga, Gladys D. C.: A model with long-term survivors: negative binomial Birnbaum-Saunders (2016)
  15. Lanjoni, Beatriz R.; Ortega, Edwin M. M.; Cordeiro, Gauss M.: Extended Burr XII regression models: theory and applications (2016)
  16. Martinussen, Torben; Holst, Klaus K.; Scheike, Thomas H.: Cox regression with missing covariate data using a modified partial likelihood method (2016)
  17. Wolter, James Lewis: Kernel estimation of hazard functions when observations have dependent and common covariates (2016)
  18. Andersen, Per Kragh; Skrondal, Anders: A competing risks approach to “biologic” interaction (2015)
  19. Blanche, Paul; Proust-Lima, Cécile; Loubère, Lucie; Berr, Claudine; Dartigues, Jean-François; Jacqmin-Gadda, Hélène: Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks (2015)
  20. Cheng, Yu-Jen; Wang, Mei-Cheng: Causal estimation using semiparametric transformation models under prevalent sampling (2015)

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