Joint Frailty-Copula Models for Tumour Progression and Death in Meta-Analysis

Fit survival data and perform dynamic prediction under joint frailty-copula models for tumour progression and death. Likelihood-based methods are employed for estimating model parameters, where the baseline hazard functions are modeled by the cubic M-spline or the Weibull model. The methods are applicable for meta-analytic data containing individual-patient information from several studies. Survival outcomes need information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression). Methodologies were published in Emura et al. (2017) , Emura et al. (2018) , Emura et al. (2019) , and Wu et al. 2020 . See also the book of Emura et al. (2019) . Survival data from ovarian cancer patients are also available.


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install.packages("joint.Cox")

3.8 by Takeshi Emura, 5 months ago


Browse source code at https://github.com/cran/joint.Cox


Authors: Takeshi Emura


Documentation:   PDF Manual  


Task views: Meta-Analysis, Survival Analysis


GPL-2 license


Depends on survival


Depended on by GFGM.copula.


See at CRAN