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

Perform likelihood estimation and dynamic prediction under joint frailty-copula models for tumour progression and death in meta-analysis. A penalized likelihood method is employed for estimating model parameters, where the baseline hazard functions are modeled by smoothing splines. The methods are applicable for meta-analytic data combining several studies. The methods can analyze data having information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression). See Emura et al. (2017) for likelihood estimation, and Emura et al. (2018) for dynamic prediction. More details on these methods can also be found in a book of Emura et al. (2019) . Survival data from ovarian cancer patients are also available.


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install.packages("joint.Cox")

3.6 by Takeshi Emura, 6 days 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