joint.Cox: 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) <doi:10.1177/0962280215604510> for likelihood estimation, and Emura et al. (2018) <doi:10.1177/0962280216688032> for dynamic prediction. More details on these methods can also be found in a book of Emura et al. (2019) <doi:10.1007/978-981-13-3516-7>. Survival data from ovarian cancer patients are also available.

Downloads:

Reverse dependencies:

Linking:

Please use the canonical form https://CRAN.R-project.org/package=joint.Cox to link to this page.