Joint Frailty-Copula Models for Tumour Progression and Death in Meta-Analysis
The Competing Risks Version of Penalized Likelihood Estimation under t...
Penalized Likelihood Estimation under the Joint Cox Models Between Tum...
Prediction of death using the Kaplan-Meier estimator
Dynamic prediction of death
Dynamic prediction of death under the joint frailty-copula model
Dynamic prediction of death under the joint frailty-copula model (the ...
Dynamic prediction of death under the joint frailty-copula model
Dynamic prediction of death under the joint frailty-copula model (the ...
I-spline basis function
Joint Frailty-Copula Models for Tumour Progression and Death in Meta-A...
Penalized Likelihood Estimation under the Joint Cox Models Between Tum...
Penalized Likelihood Estimation under the Joint Cox Models Between Tum...
Weibull-based Likelihood Estimation under the Joint Cox Models Between...
M-spline basis function
Fitting the Cox model for survival data using a penalized spline model
Simulating data from the Weibull joint frailty-copula model
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) <doi:10.1177/0962280215604510>, Emura et al. (2018) <doi:10.1177/0962280216688032>, Emura et al. (2020) <doi:10.1177/0962280219892295>, Shinohara et al. (2020) <doi:10.1080/03610918.2020.1855449>, Wu et al. (2020) <doi:10.1007/s00180-020-00977-1>, and Emura et al. (2021) <doi:10.1177/09622802211046390>. See also the book of Emura et al. (2019) <doi:10.1007/978-981-13-3516-7>. Survival data from ovarian cancer patients are also available.