joint.Cox

R package joint.Cox. Perform the Cox regression and dynamic prediction under the joint frailty-copula model between tumour progression and death for meta-analysis. The method is applicable for meta-analytic data combining several studies. The data should have information on both terminal event time (time-to-death) and non-terminal event time (time-to-tumour progression).

Keywords for this software

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References in zbMATH (referenced in 8 articles )

Showing results 1 to 8 of 8.
Sorted by year (citations)

  1. Bhattacharjee, Atanu: Bayesian approaches in oncology using R and OpenBUGS (2021)
  2. Huang, Xin-Wei; Wang, Weijing; Emura, Takeshi: A copula-based Markov chain model for serially dependent event times with a dependent terminal event (2021)
  3. Kawakami, Ryo; Michimae, Hirofumi; Lin, Yuan-Hsin: Assessing the numerical integration of dynamic prediction formulas using the exact expressions under the joint frailty-copula model (2021)
  4. Wu, Bo-Hong; Michimae, Hirofumi; Emura, Takeshi: Meta-analysis of individual patient data with semi-competing risks under the Weibull joint frailty-copula model (2020)
  5. Dörre, Achim; Emura, Takeshi: Analysis of doubly truncated data. An introduction (2019)
  6. Emura, Takeshi; Matsui, Shigeyuki; Rondeau, Virginie: Survival analysis with correlated endpoints. Joint frailty-copula models (2019)
  7. Shih, Jia-Han; Emura, Takeshi: Likelihood-based inference for bivariate latent failure time models with competing risks under the generalized FGM copula (2018)
  8. Agnieszka Król; Audrey Mauguen; Yassin Mazroui; Alexandre Laurent; Stefan Michiels; Virginie Rondeau: Tutorial in Joint Modeling and Prediction: A Statistical Software for Correlated Longitudinal Outcomes, Recurrent Events and a Terminal Event (2017) not zbMATH