R package cmprsk: Subdistribution Analysis of Competing Risks. Estimation, testing and regression modeling of subdistribution functions in competing risks, as described in Gray (1988), A class of K-sample tests for comparing the cumulative incidence of a competing risk, Ann. Stat. 16:1141-1154, and Fine JP and Gray RJ (1999), A proportional hazards model for the subdistribution of a competing risk, JASA, 94:496-509

References in zbMATH (referenced in 148 articles )

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  1. Cho, Hyunsoon; Lee, Dahhay; Lee, Sanghee; Choi, Sangbum: Applications of competing risks analysis in public health (2022)
  2. Ndlovu, Bonginkosi; Melesse, Sileshi; Zewotir, Temesgen: A regression analysis of discrete time competing risks data using a vertical model approach (2022)
  3. Zhang, Song; Qu, Yang; Cheng, Yu; Lopez, Oscar L.; Wahed, Abdus S.: Prognostic accuracy for predicting ordinal competing risk outcomes using ROC surfaces (2022)
  4. Bakoyannis, Giorgos; Chu, Fang-I.; Babiker, Abdel G. A.; Touloumi, Giota: Impact of covariate omission and categorization from the Fine-Gray model in randomized-controlled trials (2021)
  5. Ha, Il Do; Lee, Youngjo: A review of h-likelihood for survival analysis (2021)
  6. He, Yizeng; Kim, Soyoung; Kim, Mi-Ok; Saber, Wael; Ahn, Kwang Woo: Optimal treatment regimes for competing risk data using doubly robust outcome weighted learning with bi-level variable selection (2021)
  7. Kawaguchi, Eric S.; Shen, Jenny I.; Suchard, Marc A.; Li, Gang: Scalable algorithms for large competing risks data (2021)
  8. Lee, Woojoo; Do Ha, Il; Noh, Maengseok; Lee, Donghwan; Lee, Youngjo: A review on recent advances and applications of h-likelihood method (2021)
  9. Lipowski, Cäcilia; Lo, Simon M. S.; Shi, Shuolin; Wilke, Ralf A.: Competing risks regression with dependent multiple spells: Monte Carlo evidence and an application to maternity leave (2021)
  10. McLain, Alexander C.; Guo, Siyuan; Thoma, Marie; Zhang, Jiajia: Length-biased semicompeting risks models for cross-sectional data: an application to current duration of pregnancy attempt data (2021)
  11. Saha, Sudipta; Liu, Zhihui; Saarela, Olli: Instrumental variable estimation of early treatment effect in randomized screening trials (2021)
  12. van Niekerk, Janet; Bakka, Haakon; Rue, Håvard: Competing risks joint models using R-INLA (2021)
  13. Wang, Yijun; Zhang, Jiajia; Cai, Chao; Lu, Wenbin; Tang, Yincai: Semiparametric estimation for proportional hazards mixture cure model allowing non-curable competing risk (2021)
  14. Wu, Jing; Chen, Ming-Hui; Schifano, Elizabeth D.; Yan, Jun: Online updating of survival analysis (2021)
  15. Bakoyannis, Giorgos; Zhang, Ying; Yiannoutsos, Constantin T.: Semiparametric regression and risk prediction with competing risks data under missing cause of failure (2020)
  16. Chowdhury, Rafiqul I.; Islam, M. Ataharul: Prediction of risks of sequence of events using multistage proportional hazards model: a marginal-conditional modelling approach (2020)
  17. Deresa, Negera Wakgari; Van Keilegom, Ingrid: A multivariate normal regression model for survival data subject to different types of dependent censoring (2020)
  18. Hao, Meiling; Zhao, Xingqiu; Xu, Wei: Competing risk modeling and testing for X-chromosome genetic association (2020)
  19. Liu, Yi; Guo, Feng: A Bayesian time-varying coefficient model for multitype recurrent events (2020)
  20. Martens, Michael J.; Logan, Brent R.: Group sequential tests for treatment effect on survival and cumulative incidence at a fixed time point (2020)

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