cmprsk

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

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References in zbMATH (referenced in 128 articles )

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  1. Wang, Yijun; Zhang, Jiajia; Cai, Chao; Lu, Wenbin; Tang, Yincai: Semiparametric estimation for proportional hazards mixture cure model allowing non-curable competing risk (2021)
  2. Bakoyannis, Giorgos; Zhang, Ying; Yiannoutsos, Constantin T.: Semiparametric regression and risk prediction with competing risks data under missing cause of failure (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. Deresa, Negera Wakgari; Van Keilegom, Ingrid: A multivariate normal regression model for survival data subject to different types of dependent censoring (2020)
  5. Martens, Michael J.; Logan, Brent R.: Group sequential tests for treatment effect on survival and cumulative incidence at a fixed time point (2020)
  6. Sun, Xiaowei; Ding, Jieli; Sun, Liuquan: A semiparametric additive rates model for the weighted composite endpoint of recurrent and terminal events (2020)
  7. Wang, Yijun; Tang, Yincai; Zhang, Jiajia: Bayesian approach for proportional hazards mixture cure model allowing non-curable competing risk (2020)
  8. Alireza S. Mahani; Mansour T.A. Sharabiani: Bayesian, and Non-Bayesian, Cause-Specific Competing-Risk Analysis for Parametric and Nonparametric Survival Functions: The R Package CFC (2019) not zbMATH
  9. Bellach, Anna; Kosorok, Michael R.; Rüschendorf, Ludger; Fine, Jason P.: Weighted NPMLE for the subdistribution of a competing risk (2019)
  10. Choi, Sangbum; Cho, Hyunsoon: Accelerated failure time models for the analysis of competing risks (2019)
  11. Eric S Kawaguchi, Jenny I Shen, Gang Li, Marc A Suchard: A Fast and Scalable Implementation Method for Competing Risks Data with the R Package fastcmprsk (2019) arXiv
  12. Frank, Gordon; Chae, Minwoo; Kim, Yongdai: Additive time-dependent hazard model with doubly truncated data (2019)
  13. Gao, Fei; Zeng, Donglin; Couper, David; Lin, D. Y.: Semiparametric regression analysis of multiple right- and interval-censored events (2019)
  14. Hou, Jue; Bradic, Jelena; Xu, Ronghui: Inference under Fine-Gray competing risks model with high-dimensional covariates (2019)
  15. Shin, Seung Jun; Yuan, Ying; Strong, Louise C.; Bojadzieva, Jasmina; Wang, Wenyi: Bayesian semiparametric estimation of cancer-specific age-at-onset penetrance with application to Li-Fraumeni syndrome (2019)
  16. Wang, Yanzhi; Logan, Brent R.: Testing for center effects on survival and competing risks outcomes using pseudo-value regression (2019)
  17. Zhang, Feipeng; Peng, Heng; Zhou, Yong: Fine-Gray proportional subdistribution hazards model for competing risks data under length-biased sampling (2019)
  18. Zhang, Nailong; Yang, Qingyu; Kelleher, Aidan; Si, Wujun: A new mixture cure model under competing risks to score online consumer loans (2019)
  19. Zhan, Tianyu; Schaubel, Douglas E.: Semiparametric temporal process regression of survival-out-of-hospital (2019)
  20. Ahn, Kwang Woo; Banerjee, Anjishnu; Sahr, Natasha; Kim, Soyoung: Group and within-group variable selection for competing risks data (2018)

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