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. (2019) <doi:10.1177/0962280219892295>, and Wu et al. 2020 <doi:10.1007/s00180-020-00977-1>.
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.

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>, and Wu et al. (2020) <doi:10.1007/s00180-020-00977-1>.
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.

Joint Approximate Diagonalization of a set of square matrices
Different algorithms to perform approximate joint
diagonalization of a finite set of square matrices. Depending
on the algorithm, orthogonal or non-orthogonal diagonalizer is
found. These algorithms are particularly useful in the context
of blind source separation.