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.
Survival data from ovarian cancer patients are also available.

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) <10.1007/978-981-13-3516-7>.
Survival data from ovarian cancer patients are also available.

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.