Penalized Likelihood Estimation and Prediction under the Joint
Cox Models Between Tumour Progression and Death for
Meta-Analysis

Penalized Likelihood Estimation and Dynamic Prediction under the
Joint Frailty-Copula Models Between Tumour Progression and
Death for Meta-Analysis
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 (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression).

Perform the Cox regression and dynamic prediction methods under
the joint frailty-copula model between tumour progression and death for meta-analysis.
A penalized likelihood is employed for estimating model parameters, where the baseline hazard functions are approximated 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. (2015) <doi:10.1177/0962280215604510> and
Emura et al. (2017) <doi:10.1177/0962280216688032> for details.
Survival data from ovarian cancer patients are also available.

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-analysis combining several studies or for cluster survival data.

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).

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 is employed for estimating model parameters, where the baseline hazard functions are approximated 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.
Survival data from ovarian cancer patients are also available.